Probabilistic models for multi-classifier biometric authentication using quality measures

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the practioner is that the training set used to learn the parameters of the class-conditional likelihood functions is a representative sample of the unseen test set on which the system will be used. If the test set data is distorted, the assumption no longer holds and the Bayes decision rule or Maximum Likelihood rules are no longer optimal. In biometrics, the distortions of the data come from two main sources: intra-user variability, and changes in acquisition conditions. The aim of the thesis is to increase robustness of biometric verification systems to these sources of variability. We use probabilistic approaches at all stages of the biometric authentication processing chain, while taking into account the quality of the signal being modelled. We use the theoretical framework of Bayesian networks, a family of graphical models offering important flexibility. We use them both for single-classifier systems (base classifier and reliability model) and for multiple-classifier systems (classifier combination with and without quality measures).