Multi-system biometric authentication

Verifying a person's identity claim by combining multiple biometric systems (fusion) is a promising solution to identity theft and automatic access control. This thesis contributes to the state-of-the-art of multimodal biometric fusion by improving the understanding of fusion and by enhancing fusion performance using information specific to a user. One problem to deal with at the score level fusion is to combine system outputs of different types. Two statistically sound representations of scores are probability and log-likelihood ratio (LLR). While they are equivalent in theory, LLR is much more useful in practice because its distribution can be approximated by a Gaussian distribution, which makes it useful to analyze the problem of fusion. Furthermore, its score statistics (mean and covariance) conditioned on the claimed user identity can be better exploited. Our first contribution is to estimate the fusion performance given the class-conditional score statistics and given a particular fusion operator/classifier. Thanks to the score statistics, we can predict fusion performance with reasonable accuracy, identify conditions which favor a particular fusion operator, study the joint phenomenon of combining system outputs with different degrees of strength and correlation and possibly correct the adverse effect of bias (due to the score-level mismatch between training and test sets) on fusion. While in practice the class-conditional Gaussian assumption is not always true, the estimated performance is found to be acceptable. Our second contribution is to exploit the user-specific prior knowledge by limiting the class-conditional Gaussian assumption to each user. We exploit this hypothesis in two strategies. In the first strategy, we combine a user-specific fusion classifier with a user-independent fusion classifier by means of two LLR scores, which are then weighted to obtain a single output. We show that combining both user-specific and user-independent LLR outputs always results in improved performance than using the better of the two. In the second strategy, we propose a statistic called the user-specific F-ratio, which measures the discriminative power of a given user based on the Gaussian assumption. Although similar class separability measures exist, e.g., the Fisher-ratio for a two-class problem and the d-prime statistic, F-ratio is more suitable because it is related to Equal Error Rate in a closed form. F-ratio is used in the following applications: a user-specific score normalization procedure, a user-specific criterion to rank users and a user-specific fusion operator that selectively considers a subset of systems for fusion. The resultant fusion operator leads to a statistically significantly increased performance with respect to the state-of-the-art fusion approaches. Even though the applications are different, the proposed methods share the following common advantages. Firstly, they are robust to deviation from the Gaussian assumption. Secondly, they are robust to few training data samples thanks to Bayesian adaptation. Finally, they consider both the client and impostor information simultaneously.

Multi-system Biometric Authentication: Optimal Fusion and User-Specific Information

Norman Hoon Thian Poh

École Polytechnique Fédérale de Lausanne2006

Trustworthy Biometric Verification under Spoofing Attacks

Ivana Chingovska

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.

EPFL2016

Adapted Generative Models For Face Verification

Samy Bengio

It has been shown previously that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition (here we mean both identification and verification). Recently a simpler generative model, namely the Gaussian Mixture Model (GMM), was also shown to perform well. In this paper we first propose to increase the performance of the GMM approach (without sacrificing its simplicity) through the use of local features with embedded positional information; we show that the performance obtained is comparable to 1D HMMs. Secondly, we evaluate different training techniques for both GMM and HMM based systems. We show that the traditionally used Maximum Likelihood (ML) training approach has problems estimating robust model parameters when there is only a few training images available; we propose to tackle this problem through the use of Maximum a Posteriori (MAP) training, where the lack of data problem can be effectively circumvented; we show that models estimated with MAP are significantly more robust and are able to generalize to adverse conditions present in the BANCA database.