CONF Poh_03_VR_just/IDIAP Why Do Multi-Stream, Multi-Band and Multi-Modal Approaches Work on Biometric User Authentication Tasks? Poh, Norman Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2004/norman-2004-icassp.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/poh_03_vr_just:rr-03-59 Related documents Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-04) 2004 Multi-band, multi-stream and multi-modal approaches have proven to be very successful both in experiments and in real-life applications, among which speech recognition and biometric authentication are of particular interest here. However, there is a lack of a theoretical study to justify why and how they work, when one combines the streams at the feature or classifier score levels. In this paper, we attempt to cast a light onto the latter subject. Our findings suggest that combining several experts using the mean operator, Multi-Layer-Perceptrons and Support Vector Machines always perform better than the average performance of the underlying experts. Furthermore, in practice, most combined experts using the methods mentioned above perform better than the best underlying expert. REPORT Poh_03_VR_just:rr-03-59/IDIAP Why Do Multi-Stream, Multi-Band and Multi-Modal Approaches Work on Biometric User Authentication Tasks? Poh, Norman Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2003/rr03-59.pdf PUBLIC Idiap-RR-59-2003 2003 IDIAP Multi-band, multi-stream and multi-modal approaches have proven to be very successful both in experiments and in real-life applications, among which speech recognition and biometric authentication are of particular interest here. However, there is a lack of a theoretical study to justify why and how they work, when one combines the streams at the feature or classifier score levels. In this paper, we attempt to cast a light onto the latter subject. Our findings suggest that combining several experts using the mean operator, Multi-Layer-Perceptrons and Support Vector Machines always perform better than the average performance of the underlying experts. Furthermore, in practice, most combined experts using the methods mentioned above perform better than the best underlying expert.