CONF bengio:mcs:2007/IDIAP Biometric Person Authentication IS A Multiple Classifier Problem Bengio, Samy Mariéthoz, Johnny EXTERNAL http://publications.idiap.ch/attachments/papers/2007/bengio-mcs-2007.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/bengio:rr07-03 Related documents 7th International Workshop on Multiple Classifier Systems, MCS 2007 IDIAP-RR 07-03 Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is actually a multiple classifier problem as such: indeed, in order to reach state-of-the-art performance, we argue that all current systems , in one way or another, try to solve several tasks simultaneously and that without such joint training (or sharing,',','), they would not succeed as well. We explain hereafter this perspective, and according to it, we propose some ways to take advantage of it, ranging from more parameter sharing to similarity learning. REPORT bengio:rr07-03/IDIAP Biometric Person Authentication IS A Multiple Classifier Problem Bengio, Samy Mariéthoz, Johnny EXTERNAL http://publications.idiap.ch/attachments/reports/2007/bengio-idiap-rr-07-03.pdf PUBLIC Idiap-RR-03-2007 2007 IDIAP Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is actually a multiple classifier problem as such: indeed, in order to reach state-of-the-art performance, we argue that all current systems , in one way or another, try to solve several tasks simultaneously and that without such joint training (or sharing,',','), they would not succeed as well. We explain hereafter this perspective, and according to it, we propose some ways to take advantage of it, ranging from more parameter sharing to similarity learning.