Fusion of face and speech data for person identity verification
Ben-Yacoub, S.; Abdeljaoued, Y.; Mayoraz, E.
Neural Networks, IEEE Transactions on
Volume 10, Issue 5, Sep 1999 Page(s):1065 - 1074
Digital Object Identifier   10.1109/72.788647
Summary:Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion. The experimental results show that support vector machines and Bayesian classifier achieve almost the same performances, and both outperform the other evaluated classifiers

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