Compensating User-Specific Information with User-Independent Information in Biometric Authentication Tasks

Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. This is in general a binary classification task because a system either accepts or rejects an identity claim. However, a biometric authentication system contains many users. By recognizing this fact, better decision can be made if user-specific information can be exploited. In this study, we propose to combine user-specific information with user-independent information such that the performance due to exploiting both information sources does not perform worse than either one and in some situations can improve significantly over either one. We show that this technique, motivated by a standard Bayesian framework, is applicable in two levels, i.e., fusion level where multiple (multimodal or intramodal) systems are involved, or, score normalization level, where only a single system is involved. The second approach can be considered a novel score normalization technique that combines both information sources. The fusion technique was tested on 32 fusion experiments whereas the normalization technique was tested on 13 single-system experiments. Both techniques that are originated from the same principal share a major advantage, i.e., due to prior knowledge as supported by experimental evidences, few or almost no free parameter are actually needed in order to employ the mentioned techniques. Previous works in this direction require at least 6 to 10 user-specific client accesses. However, in this work, as few as two user-specific client accesses are needed, hence overcoming the learning problem with extremely few user-specific client samples. Finally, but not the least, a non-exhaustive survey on the state-of-the-arts of incorporating user-specific information in biometric authentication is also presented

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

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