Trustworthy Biometric Verification under Spoofing Attacks:Application to the Face Mode

The need for automation of the identity recognition process for a vast number of applications resulted in great advancement of biometric systems in the recent years. Yet, many studies indicate that these systems suffer from vulnerabilities to spoofing (presentation) attacks: a weakness that may compromise their usage in many cases. Face verification systems account for one of the most attractive spoofing targets, due to the easy access to face images of users, as well as the simplicity of the spoofing attack manufacturing process. Many counter-measures to spoofing have been proposed in the literature. They are based on different cues that are used to distinguish between real accesses and spoofing attacks. The task of detecting spoofing attacks is most often considered as a binary classification problem, with real accesses being the positive class and spoofing attacks being the negative class. The main objective of this thesis is to put the problem of anti-spoofing in a wider context, with an accent on its cooperation with a biometric verification system. In such a context, it is important to adopt an integrated perspective on biometric verification and anti-spoofing. In this thesis we identify and address three points where integration of the two systems is of interest. The first integration point is situated at input-level. At this point, we are concerned with providing a unified information that both verification and anti-spoofing systems use. The unified information includes the samples used to enroll clients in the system, as well as the identity claims of the client at query time. We design two anti-spoofing schemes, one with a generative and one with a discriminative approach, which we refer to as client-specific, as opposed to the traditional client-independent ones. The proposed methods are applied on several case studies for the face mode. Overall, the experimental results prove the integration to be beneficial for creating trustworthy face verification systems. At input-level, the results show the advantage of the client-specific approaches over the client-independent ones. At output-level, they present a comparison of the fusion methods. The case studies are furthermore used to demonstrate the EPS framework and its potential in evaluation of biometric verification systems under spoofing attacks. The source code for the full set of methods is available as free software, as a satellite package to the free signal processing and machine learning toolbox Bob. It can be used to reproduce the results of the face mode case studies presented in this thesis, as well as to perform additional analysis and improve the proposed methods. Furthermore, it can be used to design case studies applying the proposed methods to other biometric modes. At the second integration point, situated at output-level, we address the issue of combining the output of biometric verification and anti-spoofing systems in order to achieve an optimal combined decision about an input sample. We adopt a multiple expert fusion approach and we investigate several fusion methods, comparing the verification performance and robustness to spoofing of the fused systems. The third integration point is associated with the evaluation process. The integrated perspective implies three types of inputs for the biometric system: real accesses, zero-effort impostors and spoofing attacks. We propose an evaluation methodology for biometric verification systems under spoofing attacks, called Expected Performance and Spoofability (EPS) framework, which accounts for all the three types of input and the error rates associated with them. Within this framework, we propose the EPS Curve (EPSC), which enables unbiased comparison of systems

Lighting Normalization Algorithms for Face Verification

In this report, we address the problem of face verification across illumination, since it has been identified as one of the major factor degrading the performance of face recognition systems. First, a brief overview of face recognition together with its main challenges is made, before reviewing state-of-the-art approaches to cope with illumination variations. We then present investigated approaches, which consists in applying a pre-processing step to the face images, and we also present the underlying theory. Namely, we will study the effect of various photometric normalization algorithms on the performance of a system based on local feature extraction and generative models (Gaussian Mixture Models). Studied algorithms include the Multiscale Retinex, as well as two state-of-the-art approaches: the Self Quotient Image and an anisotropic diffusion based normalization. This last involves the resolution of large sparse system of equations, and hence different approaches to solve such problems are described, including the efficient multigrid framework. Performances of the normalization algorithms are assessed with the challenging BANCA database and its realistic protocols. Conducted experiments showed significant improvements in terms of verification error rates and are comparable to other state-of-the-art face verification systems on the same database