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TRAINING METHOD OF A PIECEWISE LINEAR CLASSIFIER FOR A MULTI-MODAL PERSON VERIFICATION SYSTEM

In this paper we propose a training method for a Piece-wise Linear (PL) binary classifier used in a multi-modal person verification system. The training criterion used minimizes the false acceptance rate as well as false rejection rate, leading to a lower Total Error (TE) made by a multi-modal verification system. The performance of the PL classifier and Support Vector Machine (SVM) binary classifier, trained using the traditional Minimum Total Misclassification Error (MTME) criterion, is compared. The PL classifier consistently outperforms the SVM classifier with the TE on average 50% lower.