Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

Interoperability of Contact and Contactless Fingerprints Across Multiple Fingerprint Sensors

Contactless fingerprinting devices have grown in popularity in recent years due to speed and convenience of capture. Also, due to the global COID-19 pandemic, the need for safe and hygienic options for fingerprint capture are more pressing than ever. However, contactless systems face challenges in the areas of interoperability and matching performance as shown in other works. In this paper, we present a contactless vs. contact interoperability assessment of several contactless devices, including cellphone fingerphoto capture. During the interoperability assessment, the quality of the fingerprints was considered using the NBIS NFIQ software with the contact-based fingerprint performing the best overall as expected. In addition to evaluating the match performance of each contactless sensor, this paper presents an analysis of the impact of finger size and skin melanin content on contactless match performance. AUC results indicate that contactless match performance of the newest contactless devices is reaching that of contact fingerprints. In addition, match scores indicate that, while not as sensitive to melanin content, contactless fingerprint matching may be impacted by finger size

An overview of touchless 2D fingerprint recognition

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

Investigating the Impact of Demographic Factors on Contactless Fingerprint Interoperability

Improvements in contactless fingerprinting have resulted in contactless fingerprints becoming a faster and more convenient alternative to contact fingerprints. The interoperability between contactless fingerprints and contact fingerprints and how demographic factors can change interoperability has been challenging since COVID-19; the need for hygienic alternatives has only grown because of the sudden focus during the pandemic. Past work has shown issues with the interoperability of contactless prints from kiosk devices and phone fingerprint collection apps. Demographic bias in photography for facial recognition could affect photographed fingerprints. The paper focuses on evaluating match performance between contact and contactless fingerprints and evaluating match score bias based on five skin demographics; melanin, erythema, and the three measurements of the CIELab color space. The interoperability of three fingerprint matchers was tested. The best and worst Area Under the Curve (AUC) and Equal Error Rate (EER) values for the best-performing matcher were an AUC of 0.99398 and 0.97873 and an EER of 0.03016 and 0.07555, respectively, while the best contactless AUC and EER were 0.99337 and 0.03387 indicating that contactless match performance can be as good as contact fingerprints depending on the device. In contrast, the best and worst AUC and EER for the cellphone contactless fingerprints were an AUC of 0.96812 and 0.85772 and an EER of 0.08699 and 0.22130, falling short of the lowest performing contact fingerprints. Demographic analysis was on the top two of the three matchers based on the top one percent of non-match scores. Resulting efforts found matcher-specific bias for melanin showing specific ranges affected by low and high melanin values. While higher levels of erythema and general redness of the skin improved performance. Higher lightness values showed a decreased performance in the top-performing matcher

Surface analysis and fingerprint recognition from multi-light imaging collections

Multi-light imaging captures a scene from a fixed viewpoint through multiple photographs, each of which are illuminated from a different direction. Every image reveals information about the surface, with the intensity reflected from each point being measured for all lighting directions. The images captured are known as multi-light image collections (MLICs), for which a variety of techniques have been developed over recent decades to acquire information from the images. These techniques include shape from shading, photometric stereo and reflectance transformation imaging (RTI). Pixel coordinates from one image in a MLIC will correspond to exactly the same position on the surface across all images in the MLIC since the camera does not move. We assess the relevant literature to the methods presented in this thesis in chapter 1 and describe different types of reflections and surface types, as well as explaining the multi-light imaging process. In chapter 2 we present a novel automated RTI method which requires no calibration equipment (i.e. shiny reference spheres or 3D printed structures as other methods require) and automatically computes the lighting direction and compensates for non-uniform illumination. Then in chapter 3 we describe our novel MLIC method termed Remote Extraction of Latent Fingerprints (RELF) which segments each multi-light imaging photograph into superpixels (small groups of pixels) and uses a neural network classifier to determine whether or not the superpixel contains fingerprint. The RELF algorithm then mosaics these superpixels which are classified as fingerprint together in order to obtain a complete latent print image, entirely contactlessly. In chapter 4 we detail our work with the Metropolitan Police Service (MPS) UK, who described to us with their needs and requirements which helped us to create a prototype RELF imaging device which is now being tested by MPS officers who are validating the quality of the latent prints extracted using our technique. In chapter 5 we then further developed our multi-light imaging latent fingerprint technique to extract latent prints from curved surfaces and automatically correct for surface curvature distortions. We have a patent pending for this method

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems

With the increasing integration of smartphones into our daily lives, fingerphotos are becoming a potential contactless authentication method. While it offers convenience, it is also more vulnerable to spoofing using various presentation attack instruments (PAI). The contactless fingerprint is an emerging biometric authentication but has not yet been heavily investigated for anti-spoofing. While existing anti-spoofing approaches demonstrated fair results, they have encountered challenges in terms of universality and scalability to detect any unseen/unknown spoofed samples. To address this issue, we propose a universal presentation attack detection method for contactless fingerprints, despite having limited knowledge of presentation attack samples. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions and minimize the variations within the live samples while enhancing the inter-class variations between the deepfake and live samples. We also conducted a comprehensive comparison of different regularizations' impact on the joint loss function for presentation attack detection (PAD) and explored the performance of a modified ResNet-18 architecture with different activation functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center loss. Finally, we evaluate the performance of the model using unseen types of spoof attacks and live data. Our proposed method achieves a Bona Fide Classification Error Rate (BPCER) of 0.12\%, an Attack Presentation Classification Error Rate (APCER) of 0.63\%, and an Average Classification Error Rate (ACER) of 0.37\%