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, eg, 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, eg, 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, eg, smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenariosThis 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

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An overview of touchless 2D fingerprint recognition

Evaluation of large-scale fingerprint search algorithms has been limited due to lack of publicly available datasets. To address this problem, we utilize a Generative Adversarial Network (GAN) to synthesize a fingerprint dataset consisting of 100 million fingerprint images. In contrast to existing fingerprint synthesis algorithms, we incorporate an identity loss which guides the generator to synthesize fingerprints corresponding to more distinct identities. The characteristics of our synthesized fingerprints are shown to be more similar to real fingerprints than existing meth- ods via eight different metrics (minutiae count - block and template, minutiae direction - block and template, minutiae convex hull area, minutiae spatial distribution, block minutiae quality distribution, and NFIQ 2.0 scores). Additionally, the synthetic fingerprints based on our approach are shown to be more distinct than synthetic fingerprints based on published methods through search results and imposter distribution statistics. Finally, we report for the first time in open literature, search accuracy against a gallery of 1 00 million fingerprints (NIST SD4 Rank-1 accuracy of 89.7%).

Fingerprint Synthesis: Search with 100 Million Prints

With the advancements in technology, smartphones’ capabilities have increased immensely. For instance, the smartphone cameras are being used for face and ocular biometric-based authentication. This research proposes finger-selfie based authentication mechanism, which uses a smartphone camera to acquire a selfie of a finger. In addition to personal device-level authentication, finger-selfies may also be matched with livescan fingerprints present in the legacy/national ID databases for remote or touchless authentication. We propose an algorithm which comprises segmentation, enhancement, Deep Scattering Network based feature extraction, and Random Decision Forest to authenticate finger-selfies. This paper also presents one of the largest finger-selfie database with over 19, 400 images. The images in the IIIT-D Smartphone Finger-selfie Database v2 are captured using multiple smartphones and include variations due to background, illumination, resolution, and sensors. Results and comparison with existing algorithms show the efficacy of the proposed algorithm which yields equal error rates in the range of 2.1 – 5.2% for different experimental protocols.

On Matching Finger-Selfies Using Deep Scattering Networks

This work presents an automated touchless fingerprint recognition system for smartphones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. Also, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1,360 touchless and touch-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our touchless scheme and the touch-based baseline scheme under constrained environmental influences. A comparative usability study on both capturing device types indicates that the majority of subjects prefer the touchless capturing method. Based on our experimental results we analyze the impact of the current COVID-19 pandemic on fingerprint recognition systems. Finally, implementation aspects of touchless fingerprint recognition are summarized.

Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects.

Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications.

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Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review

We address the problem of comparing fingerphotos, fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based fingerprint images. Development of robust versions of these technologies would enable the use of the billions of standard Android phones as biometric readers through a simple software download, dramatically lowering the cost and complexity of deployment relative to using a separate fingerprint reader. Two fingerphoto apps running on Android phones and an optical slap reader were utilized for fingerprint collection of 309 subjects who primarily work as construction workers, farmers, and domestic helpers. Experimental results show that a True Accept Rate (TAR) of 95.79 at a False Accept Rate (FAR) of 0.1% can be achieved in matching fingerphotos to slaps (two thumbs and two index fingers) using a COTS fingerprint matcher. By comparison, a baseline TAR of 98.55% at 0.1% FAR is achieved when matching fingerprint images from two different contact-based optical readers. We also report the usability of the two smartphone apps, in terms of failure to acquire rate and fingerprint acquisition time. Our results show that fingerphotos are promising to authenticate individuals (against a national ID database) for banking, welfare distribution, and healthcare applications in developing countries.

Jake Kendall

Papers

Matching Fingerphotos to Slap Fingerprint Images.