research paper on fingerprint recognition system

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• Published: 24 February 2021

An overview of touchless 2D fingerprint recognition

• Jannis Priesnitz   ORCID: orcid.org/0000-0002-0985-7735 1 ,
• Christian Rathgeb 1 ,
• Nicolas Buchmann 2 ,
• Christoph Busch 1 &
• Marian Margraf 2  

EURASIP Journal on Image and Video Processing volume  2021 , Article number:  8 ( 2021 ) Cite this article

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Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work.

1 Introduction

Fingerprints, i.e., ridge and valley patterns on the tip of a human finger, are one of the most important biometric characteristics due to their known uniqueness and persistence properties [ 1 , 2 ]. Automated touch-based fingerprint recognition has been a topic of research for several decades [ 3 ]. Nowadays, large-scale touch-based fingerprint recognition systems are not only used worldwide by law enforcement and forensic agencies, but they are also deployed in the mobile market and in nation-wide applications [ 2 , 4 ]. However, the touch-based fingerprint capturing process suffers from distinct problems, e.g., signals of low contrast caused by dirt or humidity on the sensor plate, latent fingerprints of previous users, or distortions due to elastic deformation of the finger caused by the pressure which is put on the sensor plate [ 5 ]. In addition, an inconvenient acquisition process and hygienic concerns may lower the user acceptability of touch-based fingerprint systems and hence, limit their deployment.

To tackle these shortcomings of touch-based fingerprint recognition systems, the first touchless (also referred to as contactless ) fingerprint recognition scheme was proposed by Song et al. in 2004 [ 6 ]. Since then, a constantly growing number of contributions related to this topic have been published each year by numerous research laboratories working in the field of biometrics, as illustrated in Fig.  1 . Conceptual advantages like a less constrained acquisition process pave the way for new applications, improves usability and hence, user acceptance. Further, finger images acquired by a touchless sensor exhibit no deformation and comprise no latent fingerprints. These major advantages motivated a large amount of works published in recent years.

Yearly amount of publications. Amount of publications in major conferences or journals since 2004 dedicated to the topic of touchless fingerprint recognition

This work aims at providing a comprehensive overview of published scientific literature in the field of touchless fingerprint recognition. It is not intended to re-evaluate proposed approaches as implementations of many works are not publicly available and re-implementations might lack important optimizations or require specific sensor hardware. Moreover, for technical details of surveyed approaches, the reader is referred to the according publications. Where possible, results of published works are presented in a comparative manner. If authors provided a single result in the publication text (e.g., in the abstract or summary), those values are taken directly. Otherwise, a representative result is chosen in good faith from the presented plots and tables.

While touchless fingerprint recognition technologies have been investigated for some years, the corresponding literature is dispersed across different publication media and overview works mostly focus on specific process modules. Parziale and Chen [ 7 ] elaborated on the differences of 2D and 3D acquisition technologies, processing strategies, and quality aspects. Further, the authors gave an overview on presentation attack detection (PAD) schemes. Khalil and Wan [ 8 ] reviewed state-of-the-art algorithms along the preprocessing pipeline and address PAD. Even though, their work highlights some important issues in the field it lacks a comprehensive discussion of current approaches. Labati et al. [ 5 ] conducted a comparative overview of 2D versus 3D touchless fingerprint recognition and address the processing of touchless fingerprints to touch-based equivalent fingerprints using unwrapping algorithms. Moreover, the authors provide a high-level discussion of different feature extraction and comparison subsystems. A brief survey of mobile touchless fingerprint recognition using smartphones as capturing device have been presented by Malhotra et al. [ 9 ]. Mil’shtein and Pillai [ 10 ] present a short comparative review of touchless and touch-based schemes as well as a selective summary of state-of-the-art touchless acquisition techniques. In addition, the authors briefly discuss challenges of touchless recognition. Labati et al. [ 11 ] provided a more elaborated overview of the whole recognition pipeline which is completed by a discussion of liveness detection algorithms, nonidealities of current approaches, and a performance summary.

As previously mentioned, the published overview papers are mostly restricted to particular subsets of the topic, i.e., subsystems of a touchless fingerprint recognition system.

As the fact that the existing surveys are either not comprehensive or outdated, this work aims at providing a more complete overview of the state-of-the-art of touchless 2D fingerprint recognition. The first part is structured according to the pipeline of a touchless fingerprint recognition system. It provides the reader brief overview of main processing steps, as well as a detailed summary of proposed approaches. In a second part, an in-depth discussion of issues and challenges is provided. Furthermore, available research resources are described in detail. This summary primarily addresses biometric researchers and practitioners aiming to gain an overview of the current state-of-the-art of the topic.

Apart from the standardized terms and definitions [ 12 ], the following taxonomy will be used throughout this work:

Finger image or finger photo refers to an image acquired using a touchless capture device, e.g., smartphone camera, which contains one or more fingers of a subject.

Fingerprint image refers to a finger image cropped to an area representing a fingerprint, i.e., fingertips.

Fingerprint refers to a preprocessed touchless fingerprint image or a fingerprint captured by a touch-based sensor.

Furthermore, a distinction is made between the capturing of a finger image without any preprocessing and the acquisition of a fingerprint image which includes an enhancement by some preprocessing algorithms. It should be noted that the ISO/IEC 2382 Part 37 standard suggests the usage of the term capturing process [ 12 ].

The general biometric workflow of a touchless fingerprint recognition system is sketched in Fig.  2 . The first part of this work is structured accordingly: Section 2 describes different finger image capturing approaches. In Section 3 , the processing steps which are necessary to achieve a high-quality biometric sample are described. Section 4 highlights touchless quality assessment followed by a summary of feature extraction and comparison approaches in Section 5 and Section 6 . The second part discusses different issues and challenges in Section 7 . An overview on touchless biometric databases is further given in Section 8 . Section 9 finally draws a conclusion.

Modules of fingerprint recognition. Overview on the main modules (sub-systems) of a generic touchless fingerprint recognition system

2 Capturing process

During a touchless capturing process, one or more fingers are presented to an optical capturing device. These devices can either be prototypical hardware designs assembled by the researchers or general purpose devices which are adapted to the special needs of touchless fingerprint recognition.

The National Institute of Standards and Technology (NIST) [ 13 ] published a guidance document for the evaluation of touchless fingerprint capturing. The document accurately defines requirements for the assembly of touchless fingerprint capturing devices with respect to different application scenarios.

Figure  3 depicts impressions of a fingerprint captured with a touch-based fingerprint sensor (Fig.  3 a) and a the corresponding finger image acquired using a touchless device (Fig.  3 b). It is observable that the touch-based fingerprint can be directly used for feature extraction whereas the corresponding touchless fingerprint image requires further preprocessing.

Two impressions of the same finger: a touch-based fingerprint acquired with a Crossmatch Guardian 200; b touchless fingerprint image captured with a Samsung Galaxy S8. Both images are manually cut to represent only the fingerprint area

2.1 Prototypical hardware design

Many prototypical hardware designs rely on elaborated capturing technologies adopted from other research areas to obtain finger images of high quality. Table  1 lists most relevant works categorized by approach and ordered by the year of publication. All listed approaches focus on overcoming known challenges of touchless fingerprint capturing like unconstrained environmental influences, the lack of deformations, or focusing issues.

Several authors combine a box-like setup with LEDs to achieve a predicable illumination and to exclude environmental influences [ 15 , 16 , 18 ]. LED arrangements around the finger lead to a homogeneous contrast on the fingerprint area. Colored illumination can also emphasize the fingerprint characteristics and hence lead to improved results [ 16 ].

The majority of capturing setups used finger guidance in form of circular holes [ 16 ] or fixed finger placements [ 20 ]. Tsai et al. [ 17 ] presented a more unconstrained approach which works without a box and finger guidance. The authors used a strong illumination combined with a small distance between the lens and the fingertip to minimize environmental lights. A variable-focus liquid lens was able to acquire high-quality finger images of moving fingers.

To overcome the issue of fingerprint distortions, Palma et al. [ 20 ] and Mil’shtein et al. [ 14 ] presented capturing devices using rotating line scan cameras. The acquired finger image slices were merged together to a nail-to-nail rolled fingerprint image. This impression has significantly fewer distortions than a touch-based fingerprint. Alternatively, Wang et al. [ 15 ] suggested a setup of three cameras arranged around the fingertip to acquire finger photos of different orientation which are stitched together. A continuous image analysis assessed if the finger was positioned properly and enabled a convenient capturing of high-quality finger images.

Mil’shtein et al. [ 14 ] and Ramachandra et al. [ 18 ] showed the possibility of combining the capturing of fingerprints and finger veins in multi-modal devices. Ramachandra et al. [ 18 ] used low-cost equipment such as an industrial camera with a monochrome sensor. Weissenfeld et al. [ 19 ] introduced a mobile hand-held device which captured face and finger images using a single sensor.

2.2 General purpose devices

In contrast to elaborated hardware setups, many research groups use general purpose devices to capture finger images. Most relevant approaches are summarized in Table  2 sorted by type of recording device.

First experiments on general purpose devices were conducted by Lee et al. [ 21 ] who used the camera of a mobile phone with an external LED light to acquire finger images. Hiew et al. [ 36 ] also used an external illumination along with a semi-professional camera in a box setup. In both schemes, the finger images were acquired completely manual.

Several early works investigated the applicability of webcams for finger image acquisition. Major advantages are affordable price and an easy connectivity to a computer [ 24 , 26 , 27 ]. All contributions used a manual capturing process and no additional illumination. Additionally, Piuri and Scotty [ 24 ] conducted an experiment with external illumination but were not able to achieve significant performance benefits. Nevertheless, the authors reported accurate results in a touchless to touch-based interoperability scenario. It is worth noting that despite the rather low image quality of webcams, a biometric recognition scenario could be established with such devices[ 26 ] using level-0 features. Level-0 features typically refer to local texture patterns like line structures or dominant local orientations.

Nowadays, smartphones are most often used for capturing because they are widely available, have high-quality cameras, and can provide immediate user feedback. Here, the most promising settings are to keep the auto-focus activated and if available use the macro mode. Additionally, the flash should be enabled [ 29 , 37 ]. External extensions like additional lights and macro lenses are considered as beneficial by Sagiroglu et al. [ 38 ].

Several authors suggested using on-screen finger guidance for a high user convenience and an easier fingerprint processing workflow [ 29 , 33 , 35 ]. Here the camera view presented on the screen is combined with a line representing the finger contour. Modern smartphones are able to process and qualify video streams in order to select the frame which contains a finger image of high quality [ 30 , 39 ]. A convenient automatic capturing comparable to the approach of Wang et al. [ 15 ] can be established. Moreover, Carney et al. [ 33 ] and Weissenfeld et al. [ 40 ] proposed the capturing of a whole slap hand in one image which makes the capturing of up to four fingerprints more convenient.

Several works considered finger image capturing under different environmental influences [ 32 , 41 – 43 ]. The authors concluded, that the capturing itself is not limited by different light situations or indoor and outdoor environments. Nevertheless, varying backgrounds might have a major influence on further processing.

Due to the huge variety of smartphones, several works investigated on interoperability between different models [ 28 , 34 , 41 ]. It is observable that there are no huge performance differences between particular models of the same generation. Deb et al. [ 34 ] also showed that fingerprint images acquired by low-cost smartphones could be compared to touch-based fingerprints. The tested commercial apps showed a practical biometric performance.

A nail-to-nail rolled equivalent touchless finger image is a desirable goal to achieve a large region of interest (ROI). Alkhathami et al. [ 31 ] proposed a nail-to-nail rolled finger image by mosaicking three images acquired sequentially with one smartphone. During the capturing, the subject was asked to perform a virtual rolling of his finger. All three images were stitched together to form a larger fingerprint.

Level-3 characteristics, i.e., sweat pores, on touchless image data were firstly analyzed by Genovese et al. [ 23 ]. The authors used an off-the-shelf camera and a green LED illumination. In a constrained setup with fixed distance between finger and sensor, the authors captured accurate finger images with a resolution of ≈3800 ppi which is sufficient for extracting level-3 features which refer to sweat pores.

3 Preprocessing pipeline

The captured image data differs fundamentally between touchless and touch-based acquisition devices. Most touch-based schemes produce a gray-scale image in which the ridge skin area touching the scanners surface is shown in black (or dark gray values) while valley and background area is white (or light gray values). In general, these samples are used directly for feature extraction without extensive preprocessing. The majority of touchless finger image acquisition schemes deliver color images which require a comprehensive preprocessing prior to the extraction of features. Basic challenges are a low ridge valley contrast, a blurred ROI, and a displaced, rotated, or pitched finger. Further, principally different appearances, e.g., the lack of skin deformation, cause incompatibilities. The image processing pipeline has to be developed dependent on the selected device and the observed environmental circumstances during the capturing. For an example finger image, a touchless preprocessing pipeline is illustrated in Fig.  4 . In recent years, touchless finger image preprocessing evolved to a heterogeneous topic of research with many different approaches and contributors. Unfortunately, the field lacks a harmonized vocabulary in order to compare different approaches. To get a clear understanding of the preprocessing steps, we define frequently used terms as follows:

Finger detection : in the initial step, one or more fingers are detected (or segmented), e.g., based on color or shape analysis, see Fig.  4 a–c.

Touchless preprocessing workflow. Example of a touchless preprocessing workflow based on a finger image manually taken by a Samsung Galaxy S8

Gray scale conversion, ROI extraction, and orientation estimation : the finger image is converted to gray scale and detected fingers are further cropped to extract fingerprint images which are aligned for further processing, see Fig.  4 d.

Fingerprint image enhancement : general image processing techniques are employed to improve the captured finger image, i.e., increase contrast and sharpness, see Fig.  4 e.

Further preprocessing : the finger image is enhanced to obtain fingerprints and to pronounce their features, e.g., by skeletonizing, see Fig.  4 f, g. These approaches can be directly taken from the touch-based domain and are not discussed in detail in this work.

In 2012, Khalil and Wan [ 8 ] presented a survey on the special topic of preprocessing finger images acquired with mobile phones. The authors highlighted the relevance of this field of research and summarized the differences between the touchless and the touch-based domain.

Elaborated preprocessing workflows have to be developed especially for commodity devices in order to compensate the limited capabilities of built-in cameras and environmental side effects. The following subsections summarize proposed approaches for each processing stage. Table  3 additionally highlights fundamental challenges of processing touchless finger images and lists suggested methods to overcome these challenges.

3.1 Finger detection and segmentation

Unconstrained capturing systems, which do not have a finger guidance based on dedicated hardware or an on-screen guidance, require a finger detection. Such an algorithm detects the position and orientation of the finger and forms the basis for an automatic capturing system. The image is then segmented and cut to the fingerprint containing area. Four different approaches can be distinguished, whereas in practice implementations often apply a combination of them:

Sharpness : Sharpness-based approaches exploit the difference between the focused sharp finger area and the blurred background. This effect is most suitable on images acquired with a very small finger-to-sensor distance and a wide open aperture. The early work of Lee et al. [ 49 ] presented a fixed focus real-time scheme, which selected the best focused and oriented image out of a series. The authors investigated on the suitability of general purpose focus measuring algorithms. Their experiment showed that the Variance-Modified-Laplacian of Gaussian (VMLOG) algorithm is best suited for the touchless fingerprint capturing device they used. The authors also compared a finger moving method with a fixed lens to a lens-moving method with a fixed distance between sensor and finger. They concluded that the former method is preferable which is questionable from today’s perspective. A subsequent work by the same authors [ 21 ] compared three segmentation approaches. One of them was sharpness-based and used the Tenengrad method [ 50 ] in the frequency domain. Here, a Sobel operator was used to calculate the horizontal and vertical gradients in the image. A certain threshold was established to separate the sharp foreground from the background area. Lee et al. [ 51 ] aimed at selecting the best focused image out of a video stream. The authors proposed an algorithm based on a Gaussian filter to segment the sharp regions of an image which corresponded to the finger region.

Shape : The shape of a finger is highly common for all finger position codes (i.e., various finger instances from thumb finger to little finger), which enables a detection via shape. Jonietz et al. [ 52 ] proposed a conjunction of a shape- and color-based finger detection using edge pairing. The authors applied machine learning-based algorithms to the binarized image in the LUV color model. They also used Histogram of Oriented Gradient (HOG) features with rich feature descriptors as baseline and compared their results with them.

Contrast and color : Especially, if a certain illumination is used, a determination based on the contrast or color is an efficient mechanism for finger detection. Based on findings of Hiew et al. [ 53 ] for the segmentation in skin and background area, an analysis of the YCbCr color space represents the most promising approach. The result is a binary image with a separation between finger image area and background. The above approach is widely adopted, modified to meet different prerequisites, and further investigated by many authors [ 37 , 46 , 54 , 55 ]. Ravi and Sivanath [ 27 ] showed that extending the Cr component with information of the HSV and nRGB color space enables a precise isolation of a finger. The authors used a certain threshold for every color channel and merged the results. Wang et al. [ 44 ] presented comprehensive research on different finger illuminations and color models. For this reason, the authors captured images with green, red, and blue illumination and compared the YCbCr color model with YIQ and HSV. Alternatively, other color models such as CMYK (magenta channel) [ 9 ] and CIELAB [ 39 ] were also investigated. This approach was adopted in many other preprocessing workflows similar to [ 37 , 46 , 55 ]. Because of prerequisites during the capturing process, most approaches considered only the largest segmented area as fingerprint[ 37 , 55 ]. The color-based segmentation is often combined with an adaptive thresholding, e.g., based on Otsu image thresholding [ 9 , 44 , 46 , 53 ]. Hier et al. [ 53 ] also determined the mean and covariance on the CbCr channels to improve the segmentation accuracy. Another approach by Lee et al. [ 21 ] exploited skin color properties with help of guided machine learning. This approach was shown to reveal competitive results but is more complex compared to others. As a second scheme, the authors suggested a region growing approach. Using an initial seed and a similarity measure with a certain threshold the tested pixels were added to the seed. This approach is also suitable for ROI extraction. With the mean shift segmentation Ramachandra et al. [ 41 ] proposed another contrast-based approach. The algorithm filters the input image in the spatial domain and segments it by fusing the convergence points in homogeneous regions. With this elaborated approach, the authors were able to achieve accurate results in challenging environments. Priesnitz et al. [ 56 ] presented a deep learning-based semantic segmentation scheme for the hand area as well as fingertips. The authors used a general purpose hand gesture dataset to test their algorithm against a color-based baseline segmentation algorithm. The proposed method showed accurate results especially in challenging environmental conditions. It should be critically noted that none of the discussed approaches conducted a wider analysis on different skin color types, e.g., as defined in [ 57 ].

Image depth information : Jonietz and Jivet [ 58 ] presented a segmentation approach using the information of a depth sensor combined with an RGB image captured by smartphones. The authors were able to extract the slap hand from a busy background and proposed further processing. Exploiting the images’ depth information the system worked especially well in the presence of objects of similar color, e.g., when two hands were placed on top of each other.

3.2 ROI extraction, orientation estimation, and core point detection

Once a finger is detected, the ROI has to be extracted which includes the normalization to a proper width, height, and resolution. This preprocessing stage assumes an extracted finger image as input. It should be noted that, especially in more constrained setups, finger detection and ROI extraction is done in one step [ 41 ].

In their work, Piuri and Scotti [ 24 ] simplified the color-based segmentation approach of Lee et al. [ 21 ] for ROI extraction. The authors combined this approach with a frequency estimation map. Moreover, they used a Gaussian probability density function and performed a region growing in order to extract the ROI. A comparable approach by Hiew et al. [ 53 ] exploited the ridge line characteristics of the fingertip. Here, the segmented finger was divided in non-overlapping blocks. If a ridge-line characteristic was observable within a block, it was added to the ROI. Ramachandra et al. [ 41 ] also show that in constrained setups a ROI extraction based on finger geometry properties is also possible. The authors computed the ROI statically by detecting characteristic points like the fingertip and discontinuities.

Since most feature extractors are not invariant to the rotation, all finger images must have the same orientation. Dongjae Lee et al. [ 51 ] presented a rolling and pitching estimation by calculating the distance between the core point and the border of the fingertip. Lee et al. [ 21 ] estimated the orientation by iteratively computing the robust regression method. The scheme used the Sobel operator on sub-blocks of the input image to compute the orientation of the local gradients. A simple technique on segmented finger images is to approximate a tangent along the border between finger and background and rotate the image to a predefined orientation [ 29 ]. In contrast to the aforementioned contributions, Ramachandra et al. [ 41 ] proposed a preprocessing pipeline without a rotation stage in combination with a rotation invariant feature extractor. Sisodia et al. [ 55 ] also introduced an approach which rotates minutiae features. Here, a minutia which is above a predefined correlation threshold had to be determined in the probe and reference images. Together with the core points of both images, a rotation angle was computed. Regarding an application to large scale databases, the performance of this approach is questionable.

Many comparison algorithms require a core point or a Principal Singular Point (PSP) as reference point. Several works used the ridge line orientation and curvature for detection of the core point [ 53 , 55 ]. Labati et al. [ 47 ] suggested a rather complex approach which estimates all singular points from the global ridge structure using computational intelligence classification techniques. Lee et al. [ 51 ] used the Poincaré index from the touch-based domain described in [ 59 ] to roughly determine the core point.

3.3 Fingerprint image enhancement

After the extraction of the ROI, ridge line characteristics have to be further emphasized to extract features accurately. Simple approaches only adapt fingerprint images with kernel based operations in the spatial domain [ 53 ], whereas more elaborated algorithms exploit combinations of different filters in the frequency domain [ 24 ].

Finger image enhancement should result in a fingerprint image which has a homogeneous illumination. A normalization using mean and variance filters [ 53 ] or histogram enhancements like Contrast Limited Adaptive Histogram Equalization (CLAHE) [ 46 , 60 ] were found to be well-suited for this task. Malhotra et al. [ 9 ] also suggested the analysis of Local Binary Patterns (LBP) on the ridge-valey contrast for enhancement. Moreover, Wasnik et al. [ 39 ] suggested a Frangi Filter which searches for tubular structures.

An important issue is the reduction of blur in the source image. To ensure this, Piuri and Scotti [ 24 ] proposed a combination of the Lucy-Richardson and the Wiener filter. In addition, they suggested a blind deconvolution method to enhance images which could not be handled by the algorithms proposed previously.

Liu et al. [ 60 ] combined noise removal and illumination correction, and histogram equalization in spatial domain with a ridge line frequency estimation based on Gabor filters. Additionally, a context-based correction is suggested to emphasize the ridge-line structure on low reliability areas. This approach compares blocks (patches) of the fingerprint with a directory and substitutes these blocks with more accurate data.

Birajadar et al. [ 37 ] also exploited phase congruency processing in the frequency domain. The authors use the monogenic extension of a real 2D log-Gabor isotropic wavelet for the enhancement. A later work of the same authors [ 35 ] confirmed that the algorithm also works on a large scale data set captured in an unconstrained environment. Similar work based on the aforementioned scheme was presented by Sagiroglu et al. [ 38 ].

3.4 Further preprocessing

Special capturing schemes or feature extractors require additional preprocessing steps. Image mosaicking or image fusion describes composition of two or more images to one larger finger image. In the best case, the fused image exhibits a larger ROI and a better image quality. Mosaicking techniques became essential in use-cases where a large-sized sensor is not available but a rolled finger should be captured. In the works of Choi et al. [ 61 ] and Liu et al. [ 62 ], the authors showed common use cases of mosaicking touchless images. Three (virtual) images were stitched together by using adoptions of the well-known iterative closest point algorithm. Using a very constrained capturing setup, Choi et al. [ 61 ] performed a static stitching without any correspondence measurement. The second approach by Liu et al. [ 62 ], which is also used by Alkhathami et al. [ 31 ] on a mobile device, extracts Scale Invariant Feature Transformation (SIFT) features from preprocessed images and searches for correspondences between them. Finally, the images are stitched along a border line and post-processed.

To reach the aim of touchless-to-touch image interoperability, Salum et al. [ 63 ] proposed further enhancement of touchless image data. At first, the authors added different randomly chosen ellipses to the original image. Secondly, a contour enhancement by a horizontal and vertical fading is added to the image.

Additionally, several works showed that ridge thinning and skeletonizing approaches from the touch-based domain are also applicable to touchless image data to improve the biometric performance [ 25 , 27 , 55 ].

4 Quality control

In comparison to touch-based fingerprint recognition systems, touchless schemes contain more critical steps during acquisition and processing which could reduce the system performance. For this reason, an elaborated quality assurance is particularly essential for touchless samples. Several works showed that direct application of touch-based fingerprint quality assessment leads to inaccurate results [ 64 – 66 ]. In contrast, Priesnitz et al. [ 67 ] demonstrated that the touch-based quality assessment tool NFIQ2.0 is also applicable for touchless samples. The authors concluded that the predictive power highly depends on an adequate pre-processing.

Figure  5 a depicts a finger image example of high quality in comparison to three finger images of low quality due to acquisition issues. In Fig.  5 b, the ROI contains a highlight caused by an overpowered flash light which leads to a low rigde-valley contrast while the contrast on the whole finger is rather high. A wrong focus position results in a blurry ROI from which no details are extractable as shown in Fig.  5 c. From a roll pose rotated sample depicted in Fig.  5 d, features are extractable but not comparable with an unrotated presentation.

Finger image quality. Example images of a high-quality finger image and three low-quality finger images captured by a Samsung Galaxy S8

For the purpose of quality assessment, different authors suggested dividing the fingerprint area into blocks. Subsequently, a certain quality assessment algorithm is applied to each of the blocks to either merge the results of each block to one score or to consider only areas above a certain threshold for feature extraction [ 7 , 42 , 66 , 68 ].

Parziale and Chen [ 7 ] proposed a coherence-based quality measurement. This approach measures strength of the dominant direction in a local region. For this purpose, the authors applied a normalized coherence estimation on local gradients of the gray level intensity. Moreover, the covariance matrix of the gradient vectors was denoted which represents the clarity of the ridge line structure.

Li et al. [ 42 , 65 ] introduced a quality assessment algorithm for finger images acquired with smartphones. The authors used different metrics in the spatial and frequency domain which resulted in a feature vector. A Support Vector Machine (SVM) was trained to separate high-quality blocks from those with low quality.

Yang et al. [ 66 ] presented another quality control scheme for samples captured in unconstrained environments. The input fingerprint was not previously segmented or processed. The algorithm used the amplitude-frequency and ridge line orientation in the Fourier domain as distinguishing quality feature. Each block received its own quality value, so only high-quality blocks were considered for feature extraction. The authors concluded that the proposed algorithm works accurately on the majority of tested samples but also provided finger images where it fails. The same authors extended their approach by using an SVM[ 68 ]. Li et al. [ 69 ] further extended the amount of employed quality features by additionally using a local clarity score and frequency domain analysis.

Lee et al. [ 51 ] proposed an effective early stage quality estimation method. The scheme is based on gradient distribution which shows the characteristics of the repeatable line patterns of the fingerprint and therefore its quality. For a first stage quality estimation, this scheme showed a good performance compared in relation to its computational effort. Another contribution by Noh et al. [ 16 ] proposed a comparable quality assessment and ridge frequency estimation and benchmarked its performance.

Labati et al. [ 64 ] compared their implementation of a neural network classification system with a k-Nearest-Neighbor (kNN) classifier, a linear/quadratic discriminant classifier, and NFIQ1.0 [ 70 ]. The authors used a rather constrained data set and were able to show that their own approach performs significantly better than the NFIQ1.0 algorithm. A latter work of the same authors showed the computational performance of the system in a practical approach [ 71 ].

Zaghetto et al. [ 45 ] treated rotational deviations on mosaicked fingerprints captured in a multi-view environment as a measure of quality. A four-layered neural network was proposed which classifies the input dataset into rotated or un-rotated.

5 Feature extraction

The feature extraction from touchless captured fingerprint samples is performed similarly to touch-based scenarios. Several works showed that established feature extractors can be applied to touchless image data, as shown in Fig.  6 . When using touch-based algorithms, it is important to notice that an extractor which performs considerably good on touchless and touch-based samples does not necessarily lead to an interoperability between them. Touchless developments range from simple texture feature extraction with out-of-the-box algorithms to dedicated fingerprint feature extractors.

Feature extraction. Minutiae points extracted from the touch-based fingerprint ( a ) and a touchless fingerprint ( b ). The feature extraction was performed with FingerNet [ 72 ]. Please note that due to the different capturing process, the touchless fingerprint image is mirrored

Some works in the touchless domain used the well-established Verifinger SDK to evaluate the performance of their processing pipeline [ 37 , 73 ] or benchmarked their approaches against it. Moreover, many works used the NIST standardized MINDTCT [ 74 ] algorithm for feature extractor on processed images [ 18 , 24 , 41 , 63 ]. Similarly, Yang et al. [ 66 ] used this feature extractor for quality estimation. It should be noted that Verifinger requires a fingerprint scaled to 500 DPI in order to work properly. A DPI normalization as described in Section 7.4 is usually not performed but could influence the amount of features extracted. Han et al. [ 73 ] investigated the compatibility of photographed finger images with the Verifinger feature extractor. The authors showed that it is possible to extract features with some manual preprocessing in form of a ROI extraction. It should be noted that Verifinger does perform additional internal preprocessing which improves the overall accuracy.

Sisodia et al. [ 55 ] presented a simple feature extraction technique using kernel operations which represent common minutiae characteristics. The work proposed of Ravi et al. [ 27 ] described an extraction and classification of minutiae comparable to [ 55 ] using the counting number algorithm. On the preprocessed binary image, it counts the amount of white pixel around the center point and estimates the corresponding minutia type.

Another work by Wang et al. [ 75 ] applied a sliding window on normalized images. It used local gradient codings and LBP for feature extraction. The authors analyzed different block sizes to extract the texture features. Similarly, general purpose texture descriptors have been employed in [ 76 ].

Hiew et al. [ 77 ] transferred an approach based on a block-wise Gabor-filter from the touch-based domain to touchless data. Here, the magnitude was converted to a scalar number which represents the feature point. In addition, a PCA was performed to compress the feature vector and a projection in its normalized Eigenspace is applied to each Gabor feature vector. Ramachandra et al. [ 18 ] used Spectral Minutiae Representation (SMR) on minutiae extracted with MINDTCT to achieve a fixed length feature vector.

With ScatNet, Sankaran et al. [ 32 ] and Malhotra et al. [ 9 ] proposed a novel feature extractor. Group-invariant scattering networks [ 78 ] refer to a filter bank of wavelets that produce a representation which was shown to be stable to local affine transformations. The authors extended the approach with an additional wavelet-modulus transformation for high frequency components. A low-pass filter-based convolution concatenated the wavelet responses of an arbitrary number of filters which lead to more discriminative features. The authors compared their ScatNet approach to a minutia-based baseline using VeriFinger SDK [ 79 ] and Minutiae Cylinder Code (MCC) [ 80 ] for feature extraction and performed slightly better than them.

Yin et al. [ 81 ] proposed a distortion-free feature representation using the ridge count itself as feature. Additionally, to single minutiae, pairs of minutiae were also considered as feature. The authors used a genetic algorithm to solve the combinatorial optimization problem. To improve effectiveness and accuracy, a minutia-pair expanding algorithm was suggested. To perform comparisons on these feature vectors, a similarity metric was defined. On two benchmark databases, the authors were able to perform better than the established touch-based feature extractors. It should be critically noted that in their test setup the algorithm had a high overall runtime.

Kumar and Zhou [ 26 ] suggested a feature extraction based on level-0 features, such as local texture patterns. The evaluation included various combinations of approaches, e.g., Localized Radon Transformation (LRT), and revealed remarkably good performance. In a more recent work, Vyas and Kumar [ 82 ] suggested an improved scheme using minutiae comparison.

Genovese et al. [ 23 ] proposed a combination of image processing algorithms and machine learning for extracting level-3 features (sweat pores). The authors extracted the green channel from an RGB image and applied different gamma transformations on it. A simple image processing followed by an extraction of connected components identified candidates for sweat pores. A CNN distinguished whether a candidate point is a sweat pore or not. Building upon this work, Labati et al. [ 83 ] presented a comparative study on level-3 feature extraction. Two CNNs were trained to detect sweat pores on preprocessed touchless, touch-based, and latent fingerprints. The first CNN determined possible sweat pores in the images whereas the second one detected falsely selected pores. Compared to the touch-based results, the touchless recognition performance turned out to be inferior which was caused by variable illumination situations and pore reflection.

6 Comparison

In the final comparison stage, touchless and touch-based fingerprint recognition systems operate in a similar way. Figure  7 shows a comparison of a single fingerprint captured from a touchless and a touch-based capturing device. Similar to the feature extraction stage, many works applied comparison methods of the touch-based domain, e.g., the NIST bozorth3 [ 74 ] comparator [ 41 , 63 , 84 , 85 ]. The NIST also evaluated the impact of fingerprint samples captured by touchless devices on different fingerprint recognition algorithms [ 86 ].

Minutiae comparison. Manual comparison of minutiae of a touch-based fingerprint with a mirrored touchless fingerprint

Lindoso et al. [ 87 ] introduced the first comparator dedicated to touchless fingerprint recognition in 2007. The authors proposed a zero mean normalized cross correlation approach. This method was directly applied to the gray levels of the input image. In the first step, a coarse alignment estimated the way the images were shifted and rotated to fit to the template. In the second step, fingerprint regions were selected based on quality and compared to each other based on the gray level in a final step.

Stein et al. [ 29 ] suggested a simple comparison of all minutiae to each other based on the Modified Hausdorff Distance (MHD) and orientation. Kumar and Zhou [ 26 ] compared level-0 features by using a normalized Hamming distance for an image texture comparison. The authors concluded that localized fingerprint sub-regions are more robust to rotations and partial distortions.

Labati et al. [ 88 ] presented an approach using neural networks to detect a pair of mated minutiae between two samples. A list of local features around any minutiae of the corresponding sample was established. This information was incorporated during the training of the neural network. It then decided if the candidates were referring to the same minutia or not. Also, the work includes analyses on comparing more than one fingerprint view.

Sankaran et al. [ 32 ] and Malhotra et al. [ 9 ] suggested combinations of conventional and machine learning techniques. At first, the conventional algorithm computed the L1-distance between each two ScatNet features resulting in a comparison score. Secondly, the approach relied on a supervised binary classifier which learned whether an image pair is a match or not. Building upon their work in [ 9 ], Malhorta et al. [ 89 ] showed that their algorithm can be adapted to also work on highly unconstrained data.

Lin and Kumar [ 90 ] proposed a comparison framework based on a multi-Siamese CNN for touchless to touch-based fingerprint comparison. Three sub-CNNs were trained on fingerprint minutiae, respective ridge maps, and specific regions of ridge maps. The authors generated deep fingerprint representations which were concatenated. This approach appeared to be more robust for cross-domain comparisons. They were able to outperform other CNN-based approaches. A later work by Tan and Kumar [ 91 ] especially focused on pose invariant feature matching.

To exploit the properties of their introduced features optimally, Yin et al. [ 81 ] defined a comparison metric using a number of corresponding minutiae and the global topological similarity.

7 Issues and challenges

In the past years, many works on the topic of touchless fingerprint recognition have been published. Nevertheless, there are still some unsolved issues. The following subsections set out the most relevant challenges related to the touchless recognition process and provide starting points for further research.

7.1 Biometric performance

The most important measurement criterion for any biometric system is the recognition performance. Table  4 highlights outstanding touchless fingerprint recognition workflows with their achieved recognition performance. So far, touchless 2D fingerprint schemes yield an inferior recognition accuracy compared to touch-based ones. Practical performance rates are only achieved by more sophisticated touchless approaches, e.g., based on 3D fingerprints captured by systems which utilize special acquisition devices and comprehensive preprocessing [ 92 ]. Up to now, mobile approaches using a commodity device are not able to achieve competitive results.

Along the touchless fingerprint recognition pipeline, different stages should be considered to achieve a good biometric performance:

Acquisition: A homogeneously illuminated, noise-free finger image should be acquired. High-quality camera equipment and a predictable illumination are a good precondition for a proper finger image.

Preprocessing: An accurately segmented and rotated fingerprint images yield meaningful comparison scores. At this point, user instructions or a fingerprint guidance during the capturing process can help to increase accuracy.

Quality assessment: A dedicated quality assessment which is integrated in the preprocessing pipeline is crucial to consider only samples of high quality.

Feature extraction and comparison: A specific touchless feature extraction which is adapted to the considered dataset reveals results comparable to touch-based schemes.

Also, it can be observed that some aspects of this research area have been extensively researched, while others deserve more attention. For example, several well-functioning segmentation algorithms have been proposed whereas only little research has been conducted on dedicated touchless feature extraction.

7.2 Environmental influences

Touchless fingerprint capturing and processing has to deal with different environmental influences. Environmental influences or comparison between different sensor types may lower the performance, as discussed in the following subsections. According to Malhotra et al. [ 9 ], challenging environmental situation are:

Uncontrolled background

Varying illumination

Finger position

Impurities on the finger surface

Further technical challenges can be summarized as:

Varying camera setup (especially on smartphones)

Noisy fingerprint impression due to low contrast

Especially on mobile devices, environmental influences have a high impact on the biometric recognition accuracy as showcased by Malhotra et al. [ 9 ]. Fingerprint detection and segmentation algorithms have to be robust against a huge variety of environmental conditions ranging from very dark environments to ones with bright sunlight. Especially color-based segmentation reveals deficits on scenes with a background which contains color similar to skin color. Developers working on mobile setups should be aware of the fact that an acquisition in every environmental situation is hardly feasible. Preprocessing and quality assurance algorithms should be able to assess the situation as precisely as possible and to decide whether a fingerprint capturing is feasible. An appropriate user feedback is expected to be helpful in such cases.

In prototypical hardware setups, environmental influences play a minor role. Most devices have a hood and homogeneous background which ensures a predictable illumination situation, whereas others require a laboratory environment to work properly [ 16 ].

Setups designed for the usage under different environmental influence could also benefit from the use of depth information on an image like suggested by Jonitz and Jivet [ 58 ]. The additional depth information helps algorithms to segment the finer and gives a hint on the distance between finger and sensor.

7.3 Usability and acceptability

One of the main advantages of touchless fingerprint acquisition is seen in a higher usability compared to touch-based schemes. Touch-based fingerprint capturing suffers from hygienic issues in case various participants are touching the sensor surface. Touch-based schemes also require a certain orientation and pressure of the finger and generally need more time for the capturing process. As discussed in Section 2 , touchless capturing devices show different levels of usability. In general, a higher usability can be achieved by:

Sensor-to-finger distance: A freely chosen distance between the finger and sensor during the presentation of the finger is desirable.

Pose angle: An unconstrained orientation during the presentation of the finger leads to a more convenient system.

Fourprint capturing: Most touchless devices can directly capture up to four fingers in one acquisition process. Preprocessing is then able to accurately separate the fingerprint areas into fingerprint images.

Integrated quality assessment: An integrated quality measure ensures that the capturing process is finished as soon as one high-quality template of one or more finger is captured.

Fast capturing process: The time needed to present the fingers accurately should be as short as possible. Processing steps should be applied subsequent to acquisition wherever it is feasible.

Easy-to-understand user feedback: An integrated user feedback helps to present the fingers smoothly.

The points 1–4 address an unconstrained acquisition process which is highly desirable for enhanced usability. Nevertheless, a more unconstrained capturing also requires more robust finger detection algorithms and especially an elaborated quality assessment to avoid the capturing of low-quality samples. These usability goals can only be achieved with an large amount of processing power. Today, no mobile capturing setup satisfies all of these requirements. The majority of commodity devices for capturing focus on a rather unconstrained capturing (e.g., [ 33 ]) whereas prototypical hardware setups focus more on recognition accuracy [ 16 ].

In a comprehensive study, Furman et al. [ 93 ] evaluated the usability of three stationary touchless recognition products. The authors came to the conclusion that touchless capturing requires a dedicated instruction.

7.4 Touchless-to-touch-based sensor interoperability

Interoperability between touch-based and touchless sensors is a desirable objective in many cases, e.g., to avoid re-enrolment of subjects already registered with the system in case of sensor exchange or to enable cross-matching between fingerprint databases captured through touchless and touch-based sensors. A fundamental difference between touch-based and touchless fingerprints is that touchless fingerprints are mirrored along the vertical axis. The majority of touchless sensors also capture color finger images whereas touch-based sensors capture grayscale fingerprints. Further, touchless fingerprints contain no deformations due to pressing the finger onto a surface. Some differences, e.g., mirroring, color-to-grayscale conversion or inverted back- and foreground, can be implemented in a straight-forward manner without a loss of accuracy. Other differences require elaborated approximation approaches, e.g., the aspect ratio or deformation estimation [ 94 ]. An accurate and robust scheme for correcting deformations on touchless 2D fingerprint images has not yet been established. One important factor which may cause biometric performance drops in interoperability scenarios is the DPI alignment for touchless data. For touch-based sensors the measure of spatial dot density is an important metric for acquisition devices to align the data samples to a certain size and resolution. ISO/IEC compliant fingerprints need to exhibit 500 DPI which nowadays is a minimum requirement for commercial products[ 95 ]. Touchless devices such as digital cameras feature no DPI value because the acquired image is not bound to a physical scale. Nonetheless, it is mandatory to normalize touchless fingerprints to the same size and resolution in order to achieve an accurate performance.

Fingerprint images can be normalized by cropping the image area and rescaling it to a certain height and width. By knowing the sensors resolution and focal length and approximating the distance between finger and sensor via the auto focus and the fingers’ width the DPI of the finger area can be approximated to an almost constant value [ 33 , 61 ]. Wild et al. [ 96 ] proposed a comparative test of their resolution estimation scheme on different smartphones. The authors were able to achieve accurate comparison scores in an interoperability scenario.

Another important issue is the ridge frequency estimation on touchless data. The ridge frequency of a fingerprint refers to the amount of ridges which are present within a window of defined size. Due to the touchless acquisition, there is no deformation resulting from pressing the finger onto the sensor surface. Considering 2D fingerprint images this means that the frequency of ridges is increasing towards the borders in contrast to touch-based fingerprints where it stays almost stable. Moreover, blurred border areas flatten the peaks which hampers correct feature detection. Thin plate splines are a suitable tool to correct these deformations in general which also has a positive effect on the ridge frequency and interoperability [ 16 , 48 ]. In a first approach, the algorithm of Noh et al. [ 16 ] searched for corresponding points in touchless and touch-based samples and minimizes an energy function. This approach showed accurate results but is hardly practically implementable because one touchless and one touch-based sample is needed. Lin et al. [ 48 ] went one step further and formulated a deformation correction model based on robust thin plate splines. Different models were trained to meet the individual finger shape. During the comparison different deformation correction models were automatically selected. A comparable method was also suggested by Dabouei et al. [ 97 ]. The NIST also conducted a comprehensive study on interoperability issues in application scenarios were touchless and touch-based fingerprints are compared [ 98 ].

7.5 Presentation attack detection

Reliable Presentation Attack Detection (PAD), i.e., anti-spoofing, modules are vital to enhance the security of fingerprint recognition systems. PAD represents a well-studied field of research for touch-based fingerprint recognition systems [ 99 ]. Specialized hardware-based skin detection methods which are reported to reliably detect diverse Presentation Attack Instruments (PAI) species, e.g., gummy fingers, are already integrated in many commercial touch-based fingerprint capturing devices. In contrast, in a touchless fingerprint recognition system, PAD turns out to be more challenging. Up until now, only a few approaches to PAD in touchless fingerprint acquisition have been proposed.

Moon et al. [ 100 ] proposed a PAD method based on wavelet analysis of the finger tip surface texture. Wang et al. [ 15 ] presented a PAD algorithm which exploits the differences between bona fide presentations and attack presentations in band-selective Fourier spectra. In addition, reflection detection was implemented to detect fake finger materials. A video-based PAD method based on the detection of sweat pores was presented by Parziale and Chen [ 7 ]. The idea of PAD for touchless fingerprint acquisition using texture descriptors in conjunction with neural network-based classifiers was proposed by Alkhathami et al. [ 31 ]. Moreover, a detection of finger veins can be employed for PAD in a touchless fingerprint recognition system. An approach for PAD with a setup based on smartphones is presented by Stein et al. [ 30 ]. They used a video-based acquisition and show that it is possible to detect presentation attacks by analyzing different video frames. A further work by Overgaard et al. [ 101 ] tried to exploit Eulerian Video Magnification (EVM) for liveness detection. The method emphasized the heartbeat-related color variations of genuine fingers. However, the authors raised several concerns that this approach might not be put into practice.

Taneja et al. [ 102 ] created a large publicly available spoofed fingerphoto database. The database contains print-out attacks, photo attacks, and non-spoofed finger images captured with two different smartphones.

7.6 Biometric template protection

Due to the strong and permanent link between individuals and their fingerprints, exposure of enrolled fingerprint templates to adversaries can seriously compromise biometric system security and user privacy, e.g., stolen fingerprints could be used to create artifacts in order to launch presentation attacks. Numerous techniques have been proposed for fingerprint-based biometric template protection over the last 20 years [ 103 , 104 ]. In addition, the ISO/IEC standard for the protection of biometric information [ 105 ] provides guidance for protection under requirements of confidentiality, integrity, and renewability/revocability during storage and transfer and for secure and privacy-compliant management and processing of biometric information.

While originally designed and evaluated on touch-based fingerprint databases, concepts for biometric cryptosystems, e.g., the fuzzy vault scheme [ 106 , 107 ] or the fuzzy commitment scheme [ 108 , 109 ], and cancelable biometrics, e.g., Cartesian, radial or functional transformations [ 110 , 111 ], could be adapted to touchless fingerprints, too. Depending on the employed scheme, feature type transformations of fingerprint templates might be required [ 112 ]. Due to this reason, almost no research has been conducted to design particular template protection schemes for touchless fingerprints. Most notably, Hiew et al. [ 77 ] proposed the use of multiple random projections to achieve a cancelable touchless fingerprint recognition system. Similarly, Zannou et al. [ 113 ] suggested a scheme for revocable touchless fingerprint template extraction. Lai et al. [ 114 ] presented an algorithm which directly encrypts fingerprint images using a novel memristive chaotic system. Malhotra et al. [ 115 ] addressed the issue of fingerprint template protection in selfie images on social media platforms.

7.7 Multi-biometrics

Multi-biometric systems have been found to significantly improve the accuracy and reliability of biometric systems [ 116 ]. With the possibility of a slap hand acquisition, the fusion of biometric information obtained from four fingers can be employed to improve biometric performance, especially in unconstrained environments. Deb et al. [ 34 ] demonstrated the potential of fusing information of four fingers acquired through two slap hand acquisition devices. Noh et al. [ 117 ] proposed a score-level fusion of three fingers acquired by a touchless sensor to achieve higher recognition accuracy. Carney et al. [ 33 ] performed a score-level fusion of two, four, and eight fingers. They were able to achieve significant performance gains due to the fusion.

Moreover, biometric information obtained from touchless fingerprints could be fused with different biometric characteristics. Improvement in biometric performance as a result of biometric fusion should be weighed against the associated overhead involved, such as additional sensing cost, i.e., it is preferred to combine biometric characteristics that can be acquired in a single presentation [ 118 ]. Mil’shtein et al. [ 14 ] and Ramachandra et al. [ 18 ] suggested a fusion of finger vein patterns with touchless fingerprints.

8 Research resources

Databases comprising touchless fingerprint image data are vital for the development of improved processing modules. An overview of databases available for research purposes and their properties is given in Table  5 .

The Hong Kong Polytechnic University established several databases for different proposals. So far, the most comprehensive touchless-to-touch fingerprint database has been established by Kumar [ 120 ]. It consists of 1800 touchless 2D finger images and the corresponding touch-based fingerprints acquired from 300 subjects. A multi modal database [ 121 ] features 6264 2D finger images including corresponding vein images of 156 subjects are provided with 6 samples of index and middle fingers as texture and vein image for each subject. Another database containing low-resolution finger surface images acquired by a low-cost webcam was established in [ 122 ]. The database contains 1466 images from 156 subjects captured in two sessions.

The IIITD SmartPhone Fingerphoto Database v1 (ISPFDv1) [ 32 ] is a smartphone finger photo database which consists of 4096 finger photo images from 128 subjects. The database is acquired using a smartphone camera with varying background and illumination. Per subject 8, images of both, the right index and middle finger, are taken. The illumination is categorized in indoor and outdoor whereas the background is separated into a white one and a busy one. Every category contains two fingers in two lightning and background situations. In summary, 4096 images were taken and additionally acquired with a touch-based device to estimate the cross-sensor comparison performance. A follow-up database ISPFDv2 [ 89 ] was captured using two smartphones and one touch-based device. It includes more than 17,000 touchless and 2432 touch-based samples of 304 fingers. A further extension by presentation attacks is proposed by the same institution [ 102 ]. The authors captured 128 presentation attacks using optical devices and printers.

The Social-Media Posted Finger-selfie (SMPF) database [ 102 ] provides 1000 images downloaded from social media platforms which contain fingers. This database could be used for research on template protection schemes.

Chopra et al. [ 123 ] collected another smartphone-based database. The UNconstrained FIngerphoTo (UNFIT) database contains 3450 samples of 115 subjects, captured using multiple smartphones with different resolutions. The samples are captured considering different challenges, such as background, illumination, miss-focusing and multi-finger presentations. This database is well-suited for research on finger detection and quality aspects but inappropriate for biometric performance testing.

IIT Bombay, Touchless and Touch-Based Fingerprint Database [ 35 ] consists of 800 touchless and 800 touch-based fingerprint images of 200 subjects. The touchless samples are captured using a smartphone with the developed android app and are cropped to an image size of 170 × 260. The database also consists of 800 touch-based fingerprints of the same 200 subjects with an image size 260 × 330. It aims to help researchers in their endeavors in comparing the performance of touchless and touch-based fingerprint biometric systems.

The first smartphone spoofing attack database by Taneja et al. [ 102 ] contains 4096 bonafide finger images and 8182 spoofing attacks. The bonafide images are taken from the ISPFDv1 database. From the dataset, the authors created 2048 print attacks (printouts which were again photographed) and 6144 photo attacks. The photo attacks are taken from the screens of an iPad, a smartphone, and a laptop. The authors used the same devices as in the ISPFDv1 database.

The semi-public Footnote 1 cross-sensor GUC100 database [ 124 ] contains five touch-based and one touchless sensor (TST Bird3). During the database establishment 100 subjects presented their 10 fingers to all 6 devices. This was repeated 12, to obtain natural variance. All in all approximately 72,000 images were collected.

9 Conclusions

In this work, the state-of-the-art in the constantly evolving field of touchless fingerprint recognition is summarized and discussed. This research field features a broad spectrum of different acquisition systems from high-end setups to low-cost devices. Subsequently, different preprocessing approaches have to be applied to the acquired image data. It can be observed that a general endeavor of summarized research is to achieve interoperability between touchless and touch-based fingerprint recognition systems. In general, touchless schemes reveal improved usability and high user acceptance whereas biometric performance remains as challenge, especially on mobile of-the-shelf devices. Concepts for further research topics related to touchless fingerprint recognition, e.g., PAD or biometric template protection, have already been presented in the literature. Building upon these concepts, first stationary and mobile commercial touchless fingerprint recognition systems have been introduced. However, more work is yet to be done in order to achieve robust, interoperable, secure, privacy preserving, and user-friendly systems.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

The database is not publicly available but researchers can send in their algorithms to test them on the database.

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The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the framework of MEDIAN (FKZ 13N14798). This research work has been partially funded by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE.

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A Survey of Fingerprint Recognition Systems and Their Applications

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Recognition for authentication using biometrics is an intricate pattern recognizing technique. The process is really hard to architect and design, and choosing precise algorithms competent of fetching and extracting significant features and then matching them correctly, particularly in the cases where the quality of the fingerprint images are poor quality image capturing devices are used. Problems also occur where minutia are clearly visible on very small fingerprint area that are not exactly capture by camera. It is a false assumption that fingerprint recognition is a completely settled area regarding the authentication of a person just because it always give the correct identity of an individual. Fingerprint identification remains a very complex and intricate pattern-recognition system for authentication of a person.

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Prasad, P.S., Sunitha Devi, B., Janga Reddy, M., Gunjan, V.K. (2019). A Survey of Fingerprint Recognition Systems and Their Applications. In: Kumar, A., Mozar, S. (eds) ICCCE 2018. ICCCE 2018. Lecture Notes in Electrical Engineering, vol 500. Springer, Singapore. https://doi.org/10.1007/978-981-13-0212-1_53

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Recent Progress in Visualization and Analysis of Fingerprint Level 3 Features

Dr. hongyu chen.

1 Beijing Key Laboratory for Bioengineering and Sensing Technology, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing 100083 P.R. China

Prof. Rongliang Ma

2 Institute of Forensic Science, Ministry of Public Security, Beijing 100038 P. R. China

Prof. Meiqin Zhang

Associated data.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Fingerprints provide sufficient and reliable discriminative characteristics which have been considered one of the most robust evidence for individualization. The limitation of current minutiae‐based fingerprint technology seems to be solved with the development of level 3 features since they can offer additional information for problematic fingerprint recognition and even donor profiling. So far, tremendous efforts have been devoted to detecting and analysing the third‐level details. This review summarizes the advances in level 3 details with an emphasis on their reliability assessment, visualization methods based on physical interaction, residue‐response, mass spectrometry and electrochemical techniques, as well as the potentiality for individualization, donor profiling and even other application scenarios. In the end, we also give a personal perspective on the future direction and the remaining challenges in the third‐level‐detail‐related field. We believe that the new exciting progress is expected in the development of level 3 detail detection and analysis with continued interest and attention to this field.

Level 3 features offer valuable information for individualization, donor profiling, fingerprint age determination, spoof fingerprint differentiation and even disease diagnosis, which have the potential to overcome the limitation of current minutiae‐based fingerprint technology. In this review, the progress in level 3 features is summarized with an emphasis on their reliability assessment, visualization methods, and application scenarios.

1. Introduction

Fingerprints refer to patterns on fingertips with friction ridges and recessed furrows being regularly arranged. [1] They have been regarded as one of the most valuable and solid evidence in court due to their uniqueness, immutability and permanence.[ 2 , 3 , 4 ] Fingerprints carry sufficient and reliable discriminative characteristics which ensure the acceptance of fingerprint comparison as a valid individualization method. Generally, fingerprint characteristics are classified into three dimensions, namely level 1, level 2 and level 3 features (Figure  1 ). [5] Specifically, level 1 features include the macro pattern types and ridge flows, such as loop, whorl, arch and accidental. Level 2 features give details at a deeper scale, termed Galton characteristics or minutiae points (ridge ending, enclosure, bifurcation, hook, eye, etc.). Level 3 features contain all microscopic attribute dimensions of ridges, pores, incipient ridges, warts, creases, scars, etc. [6]

Fingerprint characteristics are categorized into three levels.

Current fingerprint technology has been developed primarily focused on the first‐level and second‐level features. As we know, 6–17 minutiae (varying from country to country) guarantee the success of fingerprint recognition. [5] Nevertheless, it is not always satisfactory to process fingerprints by only employing patterns and minutiae points. The main reason lies in that fragmentary or deformed fingerprints are frequently met at crime scenes. [7] When comparing these problematic fingerprints against the prints in a database, their insufficient characteristics may cause fingerprint mismatch and thus reduce the discriminatory power. Moreover, fingerprints found in practice are often invisible, which are called latent fingerprints (LFPs) and needed to be visualized before conducting recognition. [8] It has to be pointed out, conventional fingerprint treatments may cover details and even result in pseudo characteristics, that decrease the identification accuracy. In addition, fingerprints and their level 1–2 details can be easily faked by molding methods or inkjet printing methods.[ 9 , 10 , 11 ] Thus, spoof and real samples are unable to be discriminated through the minutiae‐based fingerprint matching system.

Apart from level 1–2 features, level 3 fingerprint features are also permanent, immutable and unique. [12] Back in 1912, Locard proved that 20–40 pores are enough to give a personal identification opinion. [12] From then on, the third‐level‐feature based algorithms have been proposed and improved the performance of the recognition system to some extent.[ 13 , 14 ] Jain et al. reported that the error matching rate declined by 20 % after level 3 features were combined with level 1–2 features. [15] Recent studies indicated the third‐level features are useful for obtaining additional information about donor gender, age, race, health, etc. than just individualization. [5] Thus, level 3 details have the potential to offer a new strategy for problematic fingerprint (incomplete, deformed, or forged) recognition and even donor profiling. Unfortunately, the actual usage of level 3 details accounts for less than 1 %. [16] Investigating the reason, it is mainly that the current visualization reagents for LFPs or deposition methods cannot well display the third‐level structures. [17] Another is that the fingermarks left at crime scenes usually have poor quality, whose level 3 features are insufficient for the following identification procedure. Besides, fingerprint images are routinely captured at the resolution of 500 pixels per inch (ppi) which cannot meet the standards (≥1000 ppi) of third‐level feature extraction. [18] Last but not least, no systematic analytical methods for level 3 features have been established at home and abroad. Although high‐resolution (≥1000 ppi) fingerprint imaging techniques have driven the growth of third‐level‐feature based algorithms, there still exist some challenging issues for improving the comparison accuracy. [13]

The urgent demands for introducing level 3 features into fingerprint recognition and donor profiling have attracted not only forensic experts but also researchers from other fields. To date, considerable efforts have been devoted to detecting and analyzing the third‐level details. Therefore, it is necessary to give an overview of the recent advances in level 3 details with an emphasis on their reliability assessment, visualization methods as well as the potentiality for individualization, donor profiling and even other application scenarios. Specifically, four main sections are organized in this minireview. The first part provides a general description of the level 3 feature types and the fundamental studies on their quality and reliability. The second section introduces the multivariate techniques for detecting third‐level features involving physical interaction methods, residue‐responsive reagents, electrochemical techniques and mass spectrometry (MS) methods. The third part illustrates the application potentiality of level 3 characteristics, particularly in personal identification, donor profiling, fingerprint age determination, spoof fingerprint differentiation and even disease diagnosis. In the last part, the future directions of level 3 details detection and analysis are also outlined followed by a summary.

2. Reliability of Level 3 Features

Perception varies widely about which details fall into the level 3 categories. [19] Adopting such a view that level 3 features are everything except the fingerprint flows, patterns and minutiae points, incipient ridges, warts, creases and scars are considered as the third‐level characteristics. However, Champod holds that they should be ascribed to level 2 features because they don't require further magnification to be recognized. [20] Actually, level 3 features involve all microdimensional attributes of a ridge. Under this perspective, the incipient ridges, creases, and scars belong to level 3 features only when we focus on their microscopic details such as size, shape, length, width, angle, etc. Beyond the above controversial features, the ridge contour and width (termed ridgeoscopy), as well as pore shape, size, location, frequency and interspace (termed poroscopy), are also included in the third‐level features. Note that only employing level 2 details is incapable of problematic fingerprint comparison (fingerprints with low quality or spoof fingerprints), many researchers turn to explore the evidentiary power of level 3 details.

Nonetheless, level 3 details are easily affected by multiple factors, such as the physical conditions of donors, deposition conditions, storage circumstances, etc.[ 21 , 22 ] Hence, it is essential to clarify the reliability of level 3 features under various conditions. Generally speaking, reliability can be assessed by reproducibility and persistency, that is, whether level 3 details can be reproduced in several depositions or over a time interval. [22] Given that poroscopy and ridgeoscopy have been broadly discussed in many publications, we primarily introduce the reproducibility and persistency of the above two features followed by a detailed summary table (Table  1 ).

Reliability of level 3 features.

[a] “+” represents the parameter that can be reproduced over a time interval while “−” can not. [b]“+” represents the parameter that can be reproduced under the corresponding factors while “−” can not.

2.1. Sweat pores

Sweat pores, distributed along the papillary ridges, are formed by the duct traveling from the dermis to the epidermis. Locard claimed the pores are permanent and vary from one person to another. [12] In general, sweat pore features consist of pore size, shape, location, distribution, frequency and pore‐to‐pore interspace. Figure  2 shows the schematic measurements of the sweat pore parameters which are commonly applied in current research. Various attempts have been made to ascertain the reproducibility and persistency of sweat pores under different conditions.

Schematic measurements of sweat pore parameters.

The shape of pores can be square, triangle, round, oval or irregular. [5] It should be noted that the pore shape is usually measured by pore size or pore area. The pore size is commonly 50–265 μm in diameter. Its observed size depends on deposition or detection methods, deposition pressures, perspiration activity and fingerprint donors, etc. Ashbaugh suggested that the pore area wasn't reliable for individualization with no evidence to support his assertion. [23] One study has explored the influence of different detection methods on pore area. It advocated the pore area was unchanged in high‐quality inked prints while latent and livescan prints didn′t accurately reproduce the pore area. [24] On the contrary, the research studied by Sutton et al. showed that the pore area of inked fingerprints was not reliable, independent of the deposition substrate. [25] The group also found the parameter was variable in fingerprints developed using cyanoacrylate or ninhydrin methods. [16] Fu et al. further indicated the pore area of inked fingerprints varied when different ink quantities or deposition pressures are applied. Specifically, the pore size decreased with the ink amount or deposition pressure increasing. [21] The above results demonstrated the ink or conventional visualization methods contributed a lot to the variability of the pore area or size. As the direct microscopic imaging alternative could avoid the deposition effect and physical uncertainty, Sutton's team observed the pore area through direct fingerprint photographs and found the day had a significant impact on pore area measurement. [18] Concretely, the pore area was reproducible over one hour but not for one month. In 2011, Oklevski found the pore size of inked fingerprint samples changed over a dactyloscopy time interval of 48 years, which strengthened the unreliability of the pore area parameter. [26] Cao et al. indicated dynamic changing was individual‐dependent and occurred to the sweat pore size along with the epidermal replacement (over 28 days). [27] Dhall et al. later proved the irreproducibility of the pore area over ten consecutive days. Additionally, better pore quality was achieved on the sticky side of adhesive tape than on glass substrates. [28] Recently, Zhou et al. argued that although the collection period affected the pore size, the variation was far less obvious than the changes caused by deposition or detection methods and pressures. [29] Our recent work, published in 2021, also drew the same conclusion that the pore area was subject to high variability in different depositions. [30]

Pore frequency is another feature that fascinated researchers. Locard discovered the pore number may vary from 9–18 pores/cm ridge. [12] Another statistical analysis showed the pore density was 419–519 pores/cm 2 . [24] Gupta's group validated that the pore frequency in the periphery position of fingerprints had a significant correlation in the Index and Ring fingers. [31] The pore impressions may be open in one and closed in the other due to the difference in sweat glands secreting activity, deposition pressure, detection or capture methods, etc. One previous study suggested two inked impressions printed by the same finger displayed a large disparity in pore density. [24] The reason was explained by Luo et al. and ink deposition can′t well reflect the sweat pore number, especially for thick ink printings or donors with small pores. [32] Fu et al. proposed deposition pressure was another factor in that a pore would undergo distortion and stretch to occupy the openings if applied pressure. [21] They found the pore number was well‐reflected under low pressure and decreased with pressure increasing. Monson et al. systematically assessed the reproducibility of level 3 details over time by considering the influence of capture methods. [33] In detail, the direct photographs presented the pores whose frequency did not vary even for a ten‐year interval, while for holographic or ink rolled impressions, the pore seemed to be obscured over a one‐month observation. Additionally, livescan methods failed to display the same level details captured by the other methods, particularly the third level. Singh et al. proposed that the detected pore frequency differed considerably, which depended upon the substrate types that LFPs were deposited on, the enhancement methods that were used for processing LFPs, etc. [34] Besides, the detected number of pores had consistencies with that of the minutiae. Interestingly, livescan prints obtained every hour within eight hours indicated sweat pores didn't periodically close and open. The hypothesis was opposite to previous findings that the closed pore at one stage has been found open at another time point. They proposed the main reason was perhaps not only due to pores’ physiology but owning to ink and pressure as well. However, additional experiment data should be presented to draw such a conclusion. The observation interval (one hour) may be too long for pore activity. In other words, the pore activity was unknown during the non‐observation period, where sweat pores may periodically close and open. Hence, the pores of live scan prints should be observed within a short interval or real‐time monitored. We recently reported the number of sweat pores was consistent with that of the live fingertip and further confirmed the theory of Singh et al. that the presented pore number was the same in several depositions when we eliminated the effects of ink and pressures. [30]

The position of pores was also extensively investigated and inspired high hopes for individualization. [15] It refers to not only the relative location on the friction ridge but also pore‐to‐pore location, distance, as well as the shape they form together. The pores of inked fingerprints were reported to retain their spatial position relative to one another over 48 years. [26] Luo et al. further emphasized the pore location remained relatively stable with the pressures employed at 200 g, 600 g and 1000 g. [32] Monson et al. validated the pore location captured by direct photographs kept unchanged even for a ten‐year interval. [33] The low effect of substrates and development methods on relative pore location was also detected by Singh's group. [34] Zhou et al. subsequently published an article about the reproducibility of pore‐to‐pore distance and angle over 21 years. [29] Pore‐to‐pore angle gave an excellent reflection on the location of pore groups and was then proved to be more stable than interspace. Our group compared the frequency distribution of the distance between adjacent sweat pores in three independent depositions, whose results were consistent with earlier research. [30] Very recently, Dhall's team also suggested the pore inter‐distance and angle were found to be reliable and reproducible on glass and adhesive tape substrates. [28] Nevertheless, in the year 2020, Wang et al. discussed the pore location drift over one month. The experiment results demonstrated the pore location observed in either the direct microscopic photography or ink impression suffered the shift in both the longitudinal and transverse directions (maximum up to 166.46 μm, 61.00 μm, respectively). [35] Furthermore, the relative pore location on the friction ridge was found susceptible to the deposition pressure and secreting activity in Cao‘s work. [27]

2.2. Ridge edges, widths and incipient ridges

The level 3 details often have been limited to the consideration of pores, whereas it can be broadened to include shapes of ridge edges, ridge width as well as incipient ridges. [20]

The shape of ridge edges is classified into seven types (straight, convex, peak, table, pocket, concave and angle) by Chatterjee. The diverse shape types are formed by the differential growth of the ridge units and the pores near the ridge edge. After Oklevski examined the 100 pairs of inked impressions, it was detected that the edge feature number decreasing with the capture interval time increasing. [26] The researchers believed the susceptibility of the ridge edges to deformation and damage could account for this observation. Meanwhile, the decline of edge feature quality occurred, where the concave edge features showed the greatest stability. Our findings indicated the ridge shape was well retained on nitrocellulose (NC) membranes with a constant deposition pressure of about 250 g. [30]

Another level 3 parameter involving ridges is supposed to be ridge width which was commonly 200–500 μm. [36] Like edge shape, it was extremely vulnerable to deposition pressures and would become widen if the applied pressing force increased. [21] When the pressure was applied at less than 300 g, ridge width increased significantly, but slowly when pressure was over 300 g. [30] Besides, the variation of ridge width could be negligible by keeping constant press force. Without a doubt, width variation is also possibly attributable to physiological occurrences such as weight gain/loss, usage, gouty deformation, or age. [37]

Incipient ridges located at furrow regions, are generally thinner and lower than papillary ridges and may not be detected in fingerprint impressions. [38] Moreover, they rarely bifurcate and rarely contain pores. Stücker et al. reported that older people (>20 years old) demonstrated a higher frequency in incipient ridges than the younger group (<20 years old). [39] In the study published by Silva, the number of incipient ridges increased with age among males. [40] Different from that observed in males, there was a reduction in the number of incipient ridges among older females. Wentworth et al. found no variation was detected in incipient ridges by observing a child's inked impressions collected every two years within ten years. [41] Conversely, Monson et al. obtained a conclusion that incipient ridges of both live fingertips and ink impressions are variable even in a two‐month interval. [34] In the year 2013, Fu's group proved the pressure force played role in the reproducibility of incipient ridges. The incipient ridges were widening, distorted and even disappeared when applied excess deposition force. [21]

3. Visualization Techniques of Level 3 Features

Admittedly, the low usage rate of the third‐level features is mainly ascribed to unreliable enhancement methods for fingerprints. [17] Specifically, early fingerprint treatments aiming at the extraction of level 1 and 2 details, ignore the significance of the third‐level features, which leads to information omissions. Commonly, fingerprints left at crime scenes are invisible to our naked eyes. Although they may carry a certain amount of microscopic features, they will not be detected if no reliable visualization techniques are employed. As mentioned in section 2, there exist considerable level 3 details that can be utilized for problematic fingerprint (recognition and even donor profiling. Hence, researchers are called upon to develop reliable level 3 feature enhancement techniques and subsequent advancements have occurred in this field. Here, we classified the achievements into four categories including techniques based on physical interaction, residue‐responsive reagents, mass spectrometry (MS) methods and electrochemical techniques. As a note, only the methods which can accurately and reliably detect level 3 features will be involved in this section. To get an intuitive insight into the advances, a summary table is presented in Table  2 .

Summary of visualization techniques for level 3 features.

3.1. Techniques based on physical interaction

3.1.1. techniques based on electrostatic adsorption.

With the implementation of nanotechnology over recent years, fingerprint enhancement especially for the third‐level details has taken a step forward owing to the excellent physical and electronic properties of various nanomaterials.[ 42 , 43 , 44 ] Particularly, quantum dots (QDs) with good performance have been reported to allow LFP imaging with high contrast.[ 45 , 46 ] In the year 2017, Wu et al. utilized red‐emitting N‐acetylcysteine‐capped CdTe QDs (N−L‐Cys‐capped CdTe QDs) reagent to visualize eccrine LFPs. [7] The fingerprints deposited on aluminium foil were quickly exhibited in about 5 s after being immersed in the as‐prepared solution. The numbers of level 3 features such as sweat pores were found accurately mapped and their numbers detected were significantly larger than those processed by the cyanoacrylate agent. However, the reagent was expensive, contained toxic heavy metal ions and was prepared with complicated procedures. More importantly, the level 3 details weren't entirely detected with this QDs‐staining method rare‐earth doped luminescent nanomaterials are considered to be an alternative for visualizing LFPs on both porous and non‐porous surfaces due to their excellent fluorescent property, high chemical stability and high affinity with fingerprint residues.[ 47 , 48 ]

Nagabhushana's group realized the rapid detection of fingerprints using Sm 3+ doped calcium zirconate nanophosphors (CaZrO 3 : Sm 3+ ) prepared via an environmental‐friendly solution combustion route. [47] Notably, the sweat pore shapes of fingerprints on the glass, namely circle, triangle, open, etc. could be obviously identified. Unfortunately, this nanophosphor seemed to have no contribution to eliminating background interference and could cover the level 3 details when being excessively used. Later, this group adopted Pr 3+ activated LaOF nanophosphors (LaOF: Pr 3+ ) for imaging level 3 structures. [48] They fabricated alkali metal ions blended LaOF: Pr 3+ via the eco‐friendly ultrasound‐assisted sonochemical method and the as‐obtained product emitted bright red light under 254 nm UV light. Both the open and closed sweat pores were then detected in revealed fingerprints by SEM examination. Inevitably, it should be noted that the background hindrance could be eliminated except for substrates with background fluorescence. However, the powder reagent may adhere to pore regions due to its nonselective physical adsorption visualization mechanism. Moreover, the powder particles especially those at the nanoscale easily aggregate to a larger size which will result in the distortion of level 3 features. Additionally, the fingerprint brush could damage the fingerprint ridges during the visualization process. As a result, the powder may cover or damage some microscopic details and even cause pseudo characteristics.

3.1.2. Techniques based on hydrophilic‐hydrophobic interaction

Aggregation‐induced emission (AIE) materials have drawn extensive interest for wide applications owing to their colourful fluorescence with high contrast, low toxicity and easy functionalization. Since 2012, they have been employed to reveal LFPs along with limitations discovered in practice: (i) Organic solvents used will do damage to residues while powders harm the forensic technicians; (ii) there exist post‐treatments after fingerprint visualization, such as removing excess dye with water or air; (iii) they are customarily suitable for non‐porous substrates; and (iv) most dyes are excited under 365 nm light, which will cause damage to the technicians and fingerprint residues such as DNA.[ 49 , 50 ] To address the problems above, an AIE‐based water‐soluble probe, TPA‐1OH was designed without any cosolvent and stabilizer which could emit strong red fluorescence under visible light excitation (405 nm). [51] Its amphiphilicity made it possible to adhere to fingerprint residues through hydrophobic‐hydrophobic interaction between the lipophilic end of TPA‐1OH and the lipid secretions. Moreover, the electrostatic interaction between the positively charged TPA‐1OH and the negatively charged residues was also helpful for fingerprint enhancement. As depicted in Figure  3 , the sweat pores with a diameter of 80–120 μm were found to distribute periodically along the ridges with 100–200 μm interspace. Noteworthily, the detected pores and ridge shapes were consistent with those of live fingertips.

(a) Fluorescence microscopic images for the partial region of LFPs and the analysis of level 3 microscopic details. (b) SEM images of the fingerprint. (c) Number and location distribution of sweat pores on the bifurcation of the live fingerprint (top) and its developed fingerprint (bottom). Scale bars: 100 μm. Reproduced from Ref. [51] Copyright (2020), with permission of the American Chemical Society.

Besides AIE materials, there still exist other methods whose reagents can interact with fingerprints through hydrophilicity or hydrophobicity. In the year 2017, our group developed a fast and reliable visualization method using hydrophilic cellulose membrane and dye aqueous solution. [52] The LFPs deposited on various substrates could be detected through the pre‐treatment of membrane transference. In this approach, when the fingerprint/membrane samples were put onto the solution, the relatively hydrophobic fingerprint residues acted as a “mask” which directed dye aqueous solution to occupy the furrows and bare membrane other than ridges. Recently, we developed the sebaceous LFPs deposited on NC membranes with only water, and then the high‐resolution optical micrographs were captured. [30] From the picture in Figure  4 (a), level 3 features, including all dimensional attributes of the ridges and pores can be accurately and reproducibly extracted. Additionally, the third‐level details of water‐developed fingerprints, especially pores, ridge contours and widths, were one‐to‐one matched to those of live fingertips (Figure  4 (b)–(c)). Unfortunately, using NC membranes to lift fingerprints on problematic substrates such as skin exhibited fewer level 3 details than those directly deposited on NC membranes. [3]

(a) Optical micrographs for water‐revealed LFPs on NC membranes. Scale bars: 1000 μm in level 1 features, 500 μm in level 1 and level 2 features. (b) A direct microscopic image of a live fingertip (top) and a developed sebaceous LFP of the same fingertip on the NC membrane (bottom). The sweat pores in the yellow arrows are showing higher perspiration activities than those in the blue arrows. Scale bars: 1000 μm. (c) Dimension (marked with white double‐headed arrows) comparison of the live fingertip and water‐developed LFP/NC. Reproduced from Ref. [30] Copyright (2021), with permission of the Royal Society of Chemistry.

3.1.3. Techniques based on dissolution effect

Poly (vinyl alcohol) (PVA) materials whose properties are in favour of fingerprint preservation have attracted forensic researchers′ attention. An article published in 2020, described a super‐soft and water‐sensitive PVA electrospun nanopaper for in situ mapping the entire fingerprint characteristics at three levels (Figure  5 ). [53] The nanopaper possessed two properties that guaranteed the successful detection of fingerprint details: (i) Ultra‐softness. Once deposited on the paper, the friction ridge contacted area would be stacked while the furrow regions that didn't touch the paper would maintain fluffy; (ii) water sensitivity. A tiny amount of sweat secreted through pores could quickly and selectively dissolve the nanopaper and thereby achieved sweat pores mapping. As shown in Figure  5 (g)–(h), a systematic statistic was also conducted in this work. The results demonstrated the pore‐to‐pore distance ranged 140–300 μm and the pore sizes were about 45–52 μm. As this method exhibited excellent performance, it is urgent to discuss whether the PVA nanopaper can transfer fingerprints on various substrates.

(a) Optical image of a fingerprint mapped on the PVA nanopaper. (b) Fluorescent image of a fingerprint. (c, d) Level 2 structure (bifurcation, ending). (e) Level 3 structure (sweat pore) of the fingerprint in ridge 1–6. (f) Pixel profile of a small portion of the fingerprint. (g) Sweat pore interspacing, (h) pore size and number in ridge 1–6. (i–k) SEM images of the fingerprint mapped on the PVA nanopaper. Reproduced from Ref. [53] Copyright (2021), with permission of Elsevier.

3.2. Techniques based on residue‐responsive reagents

Endogenous fingerprint residues are mainly secreted by exocrine sweat glands and sebaceous glands including water, inorganic salts, amino acids, polypeptide, proteins, fatty acids, urea, squalene, etc. [54] It is worth noting that all the components can be the foundation of LFP visualization. Particularly, water taking up a very high proportion about 98–99 % of eccrine sweat, has led to the emergence of numerous detection methods based on water‐responsive reagents.

3.2.1. Techniques based on water‐responsive reagents

Commercial thermoplastic polyurethane (TPU) resin has a release‐induced response (RIR). In the year 2011, Chen et al. prepared TPU and fluorescein (TPU/fluorescein) electrospun mats for facile collection and identification of LFPs on various surfaces. [55] When the water in fingerprint residues contacted the TPU/fluorescein electrospun mat, a crosslinking behaviour between TPU and the residues of fingerprints led to the phase separation between the TPU network and fluorescein. As a result, the fingerprint ridges display an obvious change in color to red. Figure  6 presents the transfer procedure and effectiveness of the TPU/fluorescein electrospun mat for LFPs. The results showed that LFPs could be transferred from various surfaces and quickly developed by heating with hot air (100 °C) in 30 s, which was suited to on‐site detection of LFPs. However, the pores and ridge edge of lifted fingermarks seemed poorly enhanced when they were deposited on polypropylene film, marble and wood.

(a) The process for imaging latent fingerprints on various surfaces using the electrospun TPU/fluorescein mat. (b–g) Bright‐field images of segmental fingerprints on various indicated surfaces were obtained after heat treatment (100 °C hot air) for up to 30 s. Reproduced from Ref. [55] Copyright (2011), with permission of Wiley‐VCH.

Owing to their hydrochromic property, polydiacetylenes (PDAs) have been actively investigated for applications in humidity monitoring, water content detection of organic solvents, water‐jet‐based rewritable printing and sweat pore mapping, etc. In the year 2014, Kim and his co‐workers reported hydrochromic conjugated polymer (PDA‐coated PET film) could map the human sweat pores (Figure  7 (a‐1)). [56] Intriguingly, a tiny amount of water produced from sweat pores led to a blue‐to‐red colour change along with the fluorescence emission when the fingertip contacted the as‐prepared film. After superimposing the mapped pores on a fingerprint scanning image, they concluded that the technique could differentiate the activity of sweat pores (Figure  7 (a‐2)). However, this technique is required to screen hygroscopic elements and diacetylene monomers whose prices were expensive. In addition, the PDA films were too sensitive to enable sweat pore mapping under such environments with relative humidity over 80 %. Subsequently, this team designed a new strategy for improving the issues mentioned above. The water‐responsive fluorescein and a hydrophilic matrix polyvinylpyrrolidone (PVP) were used for sweat pore detection (Figure  7 (b‐1)). [57] Fortunately, the cost‐effective fluorescein–PVP film was stable in a wide range of humidity around 20–90 %, whereas sensitive to sweat. To further improve the property of hydrochromic films, the imidazolium containing DA monomer (DA‐1) was employed by Kim et al. [58] The chemical structure of DA‐1 and the stepwise procedure for mapping sweat pores are presented in Figure7(c). Specifically, the amphiphilic DA‐1 could be readily inkjet‐printed on conventional paper which subsequently polymerized to PDA after UV‐irradiation (30 s) and became blue as well. Once a fingertip pressed on the blue‐coloured PDA‐coated paper, an immediate colour change from blue to red as well as red fluorescence emission would happen and thus achieve sweat pore mapping on the skin. The colour of as‐produced DA‐1‐derived PDA paper maintained unchanged even in a moisture condition whose humidity was above 90 %. Undoubtedly, the pores distributed in palms, toes and soles were accurately recorded through such a PDA‐coated paper. In 2017, this group developed a polydiacetylene‐polyethylene oxide (PDA‐PEO) composite film, which underwent a blue‐to‐red colour change once encountered water (a nanolitre of sweat) and successfully achieved human sweat pores imaging. [59] Surprisingly, the flexibility of the PDA‐PEO film made it possible to visualize sweat pores of highly curved skin surfaces such as the nose.

(a–b) Contrast‐enhanced fluorescence image of a sweat pore pattern mapped on the PDA‐coated PET film (a‐1) and fluorescein‐PVP composite film (b‐1). Superimposed image of the fluorescence sweat pores (a‐2) and (b‐2) on a scanned fingerprint digital image. The black dots inside the circles indicate pores that do not secrete sweat. (a‐1) and (a‐2) reproduced from Ref. [56] Copyright (2014), with permission of Springer Nature. (b‐1) and (b‐2) reproduced from Ref. [57] Copyright (2015), with permission of The Royal Society of Chemistry. (c) Schematic illustration of Inkjet‐printable imidazolium‐modified PDA precursor for sweat pore mapping. Reproduced from Ref. [58] Copyright (2016), with permission of Wiley‐VCH.

Meanwhile, the hydrochromic carbon nanodots (CDs) create another avenue for level 3 details detection because of their unique optical properties. Shen et al. reported a supra‐CD pore mapping system by coating supra‐CDs self‐assembled by dodecyl‐functionalized CDs (CD−Ps) on filter paper. [60] Its water‐responsive behaviour was ascribed to the decomposition of the supra‐CDs when contacting water. Notably, the strong emission of supra CD‐coated paper wouldn't be extinguished even after the water evaporated.

Nevertheless, whether the as‐obtained material could be used to lift fingerprints on various substrates is still unknown. Moreover, only mapping sweat pores will cause characteristic information missing as sweat pores may vary time‐to‐time. Lanthanide metal‐organic frameworks (Ln‐MOFs), ideal candidates for sweat pore mapping, are recently designed by Zhou et al. [61] They converted into magenta light after reacting with water in a response time of 180 s. Although they offer not only pore information but also pattern type and minutiae points, the potential for the practical transference of fingerprints should be included in further investigation.

3.2.2. Techniques based on phosphate‐responsive reagents

Phosphate (Pi) is rich in eccrine sweat (1.4 mg/L). [62] Huang et al. designed a Pi‐responsive PVA electrospun nanofibrous (NFs) membrane where the assembled dual‐emission microrods of carbon quantum dots (CQDs) with Eu (III) ion ((CQDs)‐Eu (III)) are embedded (PVA/microrods). [63] The preparation procedure and application in fingerprint visualization were demonstrated in Figure  8 . The membrane had strong red emission under UV irradiation due to the aggregation‐induced Dexter energy transfer from CQDs to Eu (III) ions. When a fingertip touched the as‐prepared membrane, Pi in sweat secretions could bind with the Eu (III) ions and block the Dexter energy transfer from CQDs to Eu (III) ions, leading to the recovery of the blue fluorescence of CQDs. As a result, the ridge‐occupied area emitted a blue fluorescence under UV irradiation and even presented the sweat pore distributed along the papillary ridges. The PVA/microrods membrane could be made into paper and enabled to identify the person who touched the PVA/microrods document through fingerprint analysis. Moreover, it would be of additional value if the PVA/microrods membranes were applied to lift LFPs on various substrates.

(a) Contrast‐enhanced fluorescence image of a sweat pore pattern mapped on the PDA‐coated PET film (a‐1) and fluorescein‐PVP composite film (b‐1). Superimposed image of the fluorescence sweat pores (a‐2) and (b‐2) on a scanned fingerprint digital image. The black dots inside the circles indicate pores that do not secrete sweat. (c) Fluorescence images of the PVA/microrods NFs membrane after finger touch. Scale bar: 2000 μm. Magnified images of blue boxes showing level 2 details including crossover (1), termination (2), bifurcation (3), island (4) and level 3 details (pores in 2, 3, and 4). Reproduced from Ref. [63] Copyright (2019), with permission of the American Chemical Society.

3.2.3. Techniques based on immunolabeling reagents

Besides water, protein and polypeptide, whose content is 150–250 mg/L, have been regarded as the most abundant components in eccrine secretion. [64] To date, a few proteins including albumin, keratins 1/10, cathepsin D, dermcidin, lysozyme and EGF have been identified in fingerprints through various techniques. [1] The level 3 detection through immunolabel method dated back to the year 2009. Drapel and her co‐workers used anti‐keratin 1/10, anti‐cathepsin‐D and anti‐dermcidin to visualize fingerprints deposited on polyvinylidene fluoride (PVDF) membranes, non‐whitened papers and whitened papers. [65] The experiment results showed the revealed fingerprints on PVDF obtained the best quality. Furthermore, antigens originating from the epidermis gave well‐defined ridge edges (keratins 1 and 10; cathepsin‐D) whereas antigens secreted by sweat glands offered pore information (dermcidin). The pore mapping presented in Figure  9 was revealed by anti‐dermcidin reagents. To enhance the immunodetection signal, visible dyes, organic fluorophores and nanoparticles were later investigated to be tagged to the secondary antibody.[ 66 , 67 , 68 , 69 , 70 ] As a result, the immunolabeling application scenario for various substrates was expanded and subsequently proved to be fitted in DNA analysis. [71] Since the amount of dermcidin secreted is found to be variable and sometimes tiny, multi‐target immunolabeling approaches that can simultaneously react with several peptides have exhibited great potential for high‐quality pore visualization recently.[ 72 , 73 ]

Mixed fingerprints were printed on a PVDF membrane (a), a non‐whitened paper (b) and a whitened paper (c) which was immunodetected with anti‐dermcidin. Reproduced from Ref. [65] Copyright (2009), with permission of Elsevier.

Compared with antigen‐antibody interactions, the aptamer recognition methods open a facile pathway for the detection of level 3 details on account of their exceptionally high specificity and affinity to fingerprint residues. Liu et al. reported a lysozyme‐binding aptamer (LBA)‐modified sandwich‐structured Au/pNTP/SiO 2 surface‐enhanced Raman scattering (SERS) probe. [74] After SERS imaging, the second and third‐level details could be obviously distinguished, especially for eccrine prints on glass substrates (Figure  10 ). Noteworthily, the Au/pNTP/SiO 2 ‐LBA probe deposited on eccrine prints (Figure  10 (c)) was more than that on sebaceous prints (Figure  10 (d)) indicating the higher content of lysozyme in eccrine secretions.

(a) SERS imaging of eccrine fingerprints on a glass surface using Au/pNTP/SiO 2 ‐LBA nanoprobes. Optical images of eccrine (b‐1) and sebaceous (c‐1) fingerprints (Scale bar: 120 μm). Corresponding SERS imaging of eccrine (b‐2) and sebaceous (c‐2) fingerprints (Scale bar: 150 μm). Reproduced from Ref. [74] Copyright (2016), with permission of the American Chemical Society.

3.3. Imaging Mass spectrometry (IMS) techniques

IMS spectrometry has drawn a lot of attention in recognizing and imaging the chemical fingerprint components. Among the numerous mass spectrometry techniques, time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) and matrix‐assisted laser desorption ionization mass spectrometry (MALDI‐MS) occupy an absolute advantageous position in sensing level 3 features of fingerprints. Thus, we primarily summarized the two techniques in detail.

3.3.1. MALDI‐MS imaging techniques

Abel and Elsner utilized a MALDI‐MS technique to selectively image the interesting region of LFPs assisted by optical positioning. Promisingly, the whole acquisition time lasted for a few minutes, which was 2–3 orders of magnitude faster than conventional full MS scanning. [75] This combined optical and MS imaging could offer level 1–3 features, not just pore information. Admittedly, it is challenging to assign selected signals to physiological substances of fingerprint residues. One year later, Voelcker et al. employed the MALDI‐ToF/ToF MS technique to achieve nanostructural imaging on Ag layers (0.4–3.2 nm) coated porous wafer silicon (Ag‐coated pSi). Mass accuracy of this method was improved by more than an order of magnitude and thereby could visualize fingerprints along with their level 3 details. [76]

3.3.2. ToF‐SIMS imaging techniques

ToF‐SIMS has superior spatial resolution and do less destruction to fingerprint samples than the MALDI‐MS imaging technique. It was initially introduced by Bailey et al. with a discussion about the feasibility of fingerprint detection using such mass spectrometry method. [77] The three scenarios illustrated by this group delivered a good signal that fingerprints could be enhanced using ToF‐SIMS even for those which were poorly developed with conventional methods. In the year 2017, Graphene oxide (GO)‐enhanced ToF‐SIMS was reported to detect poison, alkaloids (>600 Da) and controlled drugs, and antibiotics (>700 Da) of relatively high mass molecules in contaminated fingerprints as well as endogenous substances (Na + , K + ). [78] Delicate fingerprint characteristics reaching the third level are obtained as presented in Figure  11 . The pore sizes, shapes and distribution could be clearly observed. The pore in Figure  11 (d) was a triangle whilst seemed round in Figure  11 (e). Another group then attempted to broaden its application to fingerprints left on the stainless steel. Results showed it was capable of identifying pore level details even for those who were deposited for 26 days. [79] Very recently, Li's group has attempted to image fingerprints on banknotes according to the signal molecular ions and fragment ion peaks of both endogenous chemicals and contaminants. [80] Certainly, the pore structure could also be captured rough substrates more than smooth surfaces. Although the methods mentioned have exceptional performance in sweat pore detection, the long scanning time might hinder the implementation in forensic investigation practice.

2D SIMS images of molecular ions of roxithromycin at m/z 837.5 in fingerprints contaminated by roxithromycin solution. (a, b) Scan area: 5000×5000 μm 2 in MacroRaster mode, 128×128 pixels. (c) Scan area: 1200×600 μm 2 in MacroRaster mode, 128×128 pixels. (d, f) Scan area: 400×400 μm 2 , 256×256 pixels in normal 2D mode. Reproduced from Ref. [78] Copyright (2017), with permission of the American Chemical Society.

3.4. Electrochemical techniques

Over the past two decades, unavoidable background interference has driven the development of electrochemical methods for fingerprint imaging. Intriguingly, two methods, electrochemiluminescence (ECL) and scanning electrochemical microscopy (SECM) techniques have already been reported to accurately and reliably visualize level 3 features owing to their sensitivity, good controllability and low toxicity. [81] Below is the achieved progress in the imaging of third‐level characteristics through those methods.

3.4.1. Fingerprint level 3 detail imaging by ECL

ECL is commonly generated by certain electrochemical reactions triggered by a potential. Su's group pioneered the application of ECL to fingerprint visualization in 2012. [82] In principle, the sebaceous residues of fingerprints on conductive substrates act as a mask or template. By spatially controlling the ECL reactions to occur in either the bare surface or ridge‐occupied area, the negative mode and positive mode of fingerprint imaging can be obtained (Figure12(a)). As demonstrated in Figure  12 (b), the sebaceous fingerprints of seven months old could be clearly visualized with partial sweat pores located along the ridges. They also performed ECL by using a highly electrochemiluminescent molecule, namely rubrene. In positive mode, the papillary ridges illuminated ECL with the dark background, eventually generating a fingerprint impression whose level 3 features could be identified. [83] Although ECL is rapid and sensitive for imaging LFPs, it is restrictive to only conductive substrates.

(a) Schematic representation of ECL imaging principle of two modes. (b) Magnified ECL images of a seven‐month‐old sebaceous fingerprint on an ITO electrode. Reproduced from Ref. [82] Copyright (2012), with permission of Wiley‐VCH. (c) Schematic representation of SECM imaging principle for label‐free LFP/NC using methyl viologen. (d, e) SECM imaging for a sebaceous LFP directly left on NC membrane (d) and lifted by NC membrane (e). Reproduced from Ref. [3] Copyright (2020), with permission of Elsevier.

3.4.2. Fingerprint level 3 detail imaging by SECM

SECM has been successfully applied to electrochemical image substrate topography and local reactivity with high resolution. It has been proven by Girault's group that silver‐stained proteins on PVDF membranes can be visualized by recording the tip current signal generated by the oxidation of the mediator K 3 IrCl 6 . [84] Afterward, this group initially performed SECM imaging on silver‐stained fingerprints. Satisfactory results were obtained despite excessive silver staining and feature being covered. [85] Our group has been working on label‐free fingerprint imaging using SECM without pre‐treatment procedures, such as silver‐staining. [3] Theoretically, the mediator methyl viologen could selectively react with the electroactive species residues rather than furrow regions, resulting in the sharp contrast current change between ridges and furrows. Figure  12 (d) and (e) illustrate the feasibility of this label‐free method for visualizing level 3 details. More interestingly, the fingerprints deposited on other surfaces such as glass could be imaged after conducting the membrane‐lifting procedure. Additionally, the imaging time could be reduced if we combined optical microscopy methods once fingerprints were transferred by the NC membrane.

4. Applications of Level 3 Features

With the rapid development of level 3 detail imaging techniques, tremendous interest has been aroused in exploring potential applications of revealed level 3 details. So far, many articles have demonstrated they are not only useful for individualization, particularly in fragmentary fingerprints, but also provide valuable information about donor profiling, fingerprint age determination, spoof fingerprint differentiation, as well disease diagnosis. In this section, we give a brief introduction to the applications that have already been investigated and then provide a detailed summary in Table  3 . It should be pointed out that the involving matching algorithm details of application in personal identification will not be included in this section as many reviews have already covered this part. [86]

Recent advances in the application of level 3 features.

4.1. Individualization

There is growing interest in utilizing level 3 details for fingerprint recognition, especially for those with fragmentary impressions. Jain et al. indicated that the error matching rate declined by 20 % when combining level 3 features with levels 1–2 features. [15] Among the various level 3 features, pores have received huge attention. Back in 1912, Locard claimed that 20–40 pores are enough to give a personal identification opinion. [12] From then on, many pore‐based matching algorithms emerged as the implementation of high‐resolution fingerprint images..[ 13 , 14 , 15 , 56 , 57 , 58 , 87 , 88 , 89 ] Since pore shapes and sizes vary from one impression to another, the pore position is mostly used in fingerprint matching and improve the comparison accuracy to some extent. Current pore‐based fingerprint comparison systems mainly rely on two algorithms: alignment‐based pore comparison algorithm and direct pore (DP) comparison algorithm. [13] Unfortunately, pore comparison is still a challenging issue because the pore alignment accuracy and only local feature extraction heavily affect the comparison result. [13] Additionally, a very limited number of studies focused on other types of level 3 features have also been reported. Jorgenson reported one fingerprint with a limited number of minutiae (3–5 minutiae) was successfully identified by a combination of shapes of edges and minutiae. [90] Reneau then published a case where a fingerprint with no minutiae was differentiated through edge shapes and secondary ridges matching. [91] Meanwhile, substantial efforts were devoted to exploring algorithms relying on ridge counter, incipient ridge, and creases.[ 15 , 92 , 93 , 94 , 95 ]

Our group recently proposed a new parameter termed “frequency distribution of the distance between adjacent sweat pores” (FDDasp), which was used for describing the pore‐to‐pore location. [30] The parameter was highly identifiable and thus applied to differentiate two fingerprint fragments whose minutiae were the same. As Figure  13 illustrated, the pore‐to‐pore distances of the two fragments were not consistent. In combination with other characteristics such as edge shape, we ultimately gave an opinion that the fingerprints were from different fingertips. In the future study, more fingerprint samples should be included to further verify the identifiability of the proposed parameter. Meanwhile, larger area of one fingerprint sample should be statistically explored such as the FDDasp in different regions of the same fingerprint.

(a, b) Segments of two different fingerprints. (c) Frequency distribution of the distance between two adjacent sweat pores distributed along the ridge in a and b. Scale bars: 200 μm. Reproduced from Ref. [30] with permission of the Royal Society of Chemistry.

4.2. Donor profiling and fingerprint age determination

Fingerprint level 3 details offer additional information than just identification such as donor gender, donor age, donor race, the time since fingerprint deposition, etc. Hence, it is of vital importance to provide related research below.

The value of the level 3 feature in sex determination has been evaluated in several studies. Nagesh et al. examined included the fingerprint samples of 230 Indians and reported there was no significant difference in the sweat pore sizes and frequency between both the males and females. [96] To be specific, the pore frequency of females and males were 8.40 and 8.83 pores/cm ridge, respectively. The pore sizes of males ranged from 69 μm to 284 μm and those of females were 66–287 μm. Preethi et al. found pore number less than or equal to 32 pores/cm 2 was more likely to be of male origin, whereas more than or equal to 36 pores/cm 2 was more likely to be of female origin. No significant difference was detected in pore types and shapes. [97] Kumar and his co‐workers conducted a study to observe the pore shapes of left thumb ink impressions. [98] They found the pore number was 2–4 pores/cm ridge, which demonstrated no difference in both males and females. However, circular or round pores possessed higher occurrence in males than females. Wang et al. detected the shift of pore location that the maximum longitudinal and transverse location shifts of males were 166.46 μm and 61.00 μm while those of the females were 73.08 μm and 45.88 μm. [99] Additionally, another study undertaken by the same group also indicated the pore sizes of males were larger than those of females. [27] Murlidharf concluded that ridge shapes had a certain advantage over the poroscopy in sex determination. The reason may be because the sample number of males whose 1 cm ridge had one concave edge was higher than that of females. [100]

Level 3 details were found useful in age determination. Nagesh et al. found pore size gradually increased, and the pore position and pore shape varied with the age. [96]

Level 3 features also have a relation to group differentiation. Singh et al. studied fingerprints deposited from Brahmins and Rajputs of Himachal Pradesh. They finally concluded that the pore size was different in both communities while no significant difference in pore frequency, interspacing, size, shape, and position. [101] Very recently, in the work of Govindarajulu et al., the ridge width of eleven criminals was found to vary in right and left hands while no significant differences were detected in normal people. [102]

It has been observed that ridge topography may change with latent fingerprints age advanced. [103] It has been reported by Preda and his co‐workers that the ridge suffered narrowing and a loss in ridge continuity over time. [104]

4.3. Other applications

In addition to the above applications, level 3 features were applied to ascertain if a fingerprint is a forgery. Champod et al. proved the presence or absence of pores could be reasonably used in discriminating genuine from spoof transactions. [105] Additionally, several studies have indicated that the pore characteristics are associated with some sweating‐related medical diseases and thus have the potential to early diagnose such diseases.[ 106 , 107 ]

5. Summary and Future Prospects

Fingerprints carry sufficient and reliable discriminative characteristics which ensure their status in individualization. Over the past years, advances in analytical instruments and new technologies have accelerated the development of forensic chemistry, especially in level 3 characteristic detection and analysis. The visualization and application of level 3 features prove that the third‐level features give additional information (gender, age, race, health, etc.) about the donor than just individualization.

In this review, four main sections are organized. The first part provides a brief introduction of the level 3 feature types along with the assessment of their quality and reliability. The second section summarizes the related techniques for detecting third‐level features such as physical interaction methods, residue‐responsive reagents, MS methods and electrochemical techniques. The third part highlights the application of level 3 characteristics, especially in personal identification, donor profiling (age, sex, race, etc.), fingerprint age determination, spoof fingerprint discrimination and even the diagnosis of sweat‐related disease.

Although considerable state‐of‐the‐art achievements have been attained in the third‐level related field, the third‐level details are rarely utilized during the fingerprint identification process. [14] The reasons are listed below: (i) The current visualization reagents for LFPs or deposition methods cannot well display the third‐level structures. [15] For example, the powder reagent is frequently used for latent fingermark visualization relying on the electrostatic adsorption between the powder and fingerprint residues. Unfortunately, the powder easily aggregates and inevitably adheres to certain pore regions, resulting in the distortion of some microscopic details as well. Additionally, the traditional ink deposition method will contaminate fingertips and more importantly, excess ink will cover the level 3 features. (ii) Usually, the fingermarks are left at crime scenes with poor quality, whose level 3 features are insufficient or not well‐reflected and thus can't be extracted for the following identification procedure. (iii) Besides, fingerprint images in fingerprint databases are routinely captured at the resolution of 500 ppi which cannot meet the standards of third‐level feature extraction. In such a situation, the comparison can not be achieved even if the fingermarks at crime scenes possess enough level 3 features. (iv) Last but not least, no systematic and mature analytical methods have been developed for level 3 features. Although the emerging high‐resolution (≥1000 ppi) fingerprint imaging techniques have facilitated the growth of third‐level‐feature based algorithms, it is still a long way to go for improving the comparison accuracy. For instance, the pore alignment accuracy and only local feature extraction heavily affect the comparison result of pore‐based algorithms.

Hence, several challenging issues need to be resolved before the implementation of level 3 features. Specifically, (i) developing reliable visualization methods that allow effective extraction of level 3 features. First, as PVA‐based or PDA‐based papers exhibited exceptional performance in sweat pore mapping, whether they can be an alternative to forensic tape should be clarified in the future work. Second, some novel detection techniques such as MS imaging and SECM techniques show outperformance in level 3 feature detection, nevertheless, their long scanning time might hinder the implementation in forensic investigation practice. Hence, it is urgent to develop time‐saving imaging strategies such as changing the scanning path into the zigzag or spiral mode to enable large‐area imaging. Additionally, novel tips such as soft probes should be explored for using SECM to scan delicate samples with topographic sample features. Third, the compatibility of mentioned detection techniques with DNA analysis should constitute the further development steps to be investigated. (ii) Utilizing high‐resolution fingerprint imaging or capture techniques in fingerprint database construction. Only in this way, can level 3 features of fingerprint samples in the database be extracted and compared with the fingermarks at crime scenes. (iii) Exploring multi parameters for the third level detail analysis and improving the accuracy of level‐3‐feature‐based algorithms. The concept of level 3 details is often limited to the sweat pores which easily leads to information missing, whereas it can be broadened to ridge counters, such as the angle of bifurcations. We believe the application scenario can be expanded as more level 3 parameters are systematically investigated. (iv) Establishing standard measurement methods. Current research adopted various measurement methods of level 3 parameters. Under such circumstances, the comparison among different studies cannot be achieved. Hence, it is urgent to find out a scientific measurement method and unify it in future work. (v) As many fingerprint samples as possible should be investigated to screen out the characteristic parameters and verify the accuracy of prediction results as well. (vi) Focusing not only on level 3 details but other fingerprint information. Since the analysis methods of the third level details are still evolving and many mentioned techniques provide the chemical information of residues more than just physical image patterns, more parameters involving fingerprint patterns, minutiae and chemical components should be simultaneously considered and combined with level 3 features to support more robust individualization, donor profiling, spoof fingerprint differentiation, etc.

Conflict of interest

The authors declare no conflict of interest.

Biographical Information

Hongyu Chen received her masters degree in Forensic Science at Criminal Investigation Police University of China in 2020. She is now a PhD student at School of Chemistry and Biological Engineering, University of Science and Technology Beijing. Her research interests mainly focus on the visualization and analysis of multidimensional information in latent fingerprints such as level 3 features, endogenous/exogenous fingerprint residues, donor profiling, fingermark age and DNA profiles .

Rongliang Ma is now a professor at Institute of Forensic Science, Ministry of Public Security in Beijing. He studied at Centre for Forensic Science, University of Technology Sydney (UTS), Australia and received his PhD from UTS in 2012. His research interest and activities include the detection of latent fingermarks, the practice and theories of fingerprint identification and Automated Fingerprint Identification System (AFIS), and fingerprint intelligence. He is a member of the Interpol AFIS Expert Working Group and the Chair of Fingerprint Workgroup (FW) of Asian Forensic Science Network (AFSN). He is also an adjunct professor of several Chinese Universities .

Meiqin Zhang is currently a professor at University of Science and Technology Beijing. After a PhD at Peking University in 2006, she pursued her research as a postdoc at the Ecole Polytechnique Fédérale de Lausanne (Switzerland) from 2006 to 2007 and a researcher of ‘Marie Curie Incoming International Fellowship’ at the University of Warwick (UK) from 2007 to 2009. Her research activities include electrochemistry at liquid‐liquid interfaces, latent fingerprints development and imaging, development and application of scanning electrochemical microscopy .

Acknowledgements

The authors acknowledge the financial support from A Project Funded by Yao Liu, Academician of China Academy of Engineering (No. 2022‐YZ‐01) and the National Natural Science Foundation of China (No. 21727815).

H. Chen, R. Ma, M. Zhang, ChemistryOpen 2022 , 11 , e202200091. [ PMC free article ] [ PubMed ]

Contributor Information

Prof. Rongliang Ma, Email: moc.361@3102lram .

Prof. Meiqin Zhang, Email: nc.ude.btsu@niqiemgnahz .

Data Availability Statement