Fingerprint technology has evolved immensely since its initial use in the 1800s when it was used solely to assist with crime investigations. It is now used as a convenience replacing passwords and PIN numbers from logging into bank accounts, mobile devices, gaining access into rooms and various other processes where time plays a key factor. This chapter focuses on how fingerprint technology can be used to improve speed and accuracy of certain processes, i.e. exams as the society accepts this as part of everyday life. In particular, we look at a use case in an educational setting where youngsters are much used to digital technologies as part of their daily life.

Advances in Fingerprint Technology

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

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

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.

Matching Fingerphotos to Slap Fingerprint Images.

Touchless technologies for fingerphoto recognition based on smartphones can be considered selfie biometrics, in which a user captures images of his or her own biometric traits by using the integrated camera in a mobile device (here referred to as selfie fingerprint biometrics). Such systems mitigate the limitations of leaving latent fingerprints, dirt on the acquisition device released by the fingers, and skin deformations induced by touching an acquisition surface associated with a touch ID-based system. Furthermore, the use of the integrated camera to perform biometric acquisition bypasses the need of a dedicated fingerprint scanner. With respect to touch-based fingerprint recognition systems, selfie fingerprint biometrics require ad hoc methods for most steps of the recognition process. This is because the images captured using smartphone cameras present more complex backgrounds, lower visibility of the ridges, reflections, perspective distortions, and nonuniform resolutions. Selfie fingerprint biometric methods are usually less accurate than touch-based methods, but their performance can be satisfactory for a wide variety of security applications. This chapter presents a comprehensive literature review of selfie fingerprint biometrics. First, we introduce selfie fingerprint biometrics and touchless fingerprint recognition methods. Second, we describe the technological aspects of the different steps of the recognition process. Third, we analyze and compare the performances of recent methods proposed in the literature.

/pdf/a-scheme-for-fingerphoto-recognition-in-smartphones-50w3b8wqcj.pdf

A scheme for fingerphoto recognition in smartphones

Biometric-based authentication for smart handheld devices promises to provide a reliable and alternate security mechanism compared to traditional methods such as pins, patterns, and passwords. Although fingerprints are a viable source for authentication, they generally require installation of an additional hardware such as optical and swipe sensors on mobile devices, and are only available in expensive, high-end smartphones. Alternatively, fingerphoto images captured using the smartphone camera for authentication is one of the promising biometric approaches. However, using fingerphotos for authentication brings along a major challenge of fingerphoto spoofing. This research is aimed at understanding the effect of spoofing on fingerphotos. There are three major contributions of this research: (i) create a large spoofed fingerphoto database and make it publicly available for research, (ii) to establish the effect of print attack and photo attack in fingerphoto spoofing, and (iii) understand the performance of existing spoofing detection algorithms on fingerphoto spoofing.

Archit Taneja

Papers

Fingerphoto spoofing in mobile devices: A preliminary study