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
https://publications.idiap.ch/attachments/reports/2004/norman-2004-icassp.pdf
PUBLIC
https://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
https://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.