Feature distribution modelling techniques for 3D face verification

Authors:
Chris McCool;Jordi Sanchez-Riera;Sébastien Marcel
Affiliations:
Idiap Research Institute, P.O. Box 592, CH-1920 Martigny, Switzerland;Idiap Research Institute, P.O. Box 592, CH-1920 Martigny, Switzerland;Idiap Research Institute, P.O. Box 592, CH-1920 Martigny, Switzerland
Venue:
Pattern Recognition Letters
Year:
2010

Citing 6
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Abstract

This paper shows that Hidden Markov models (HMMs) can be effectively applied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian mixture model (GMM) technique. Experiments conducted on the Face Recognition Grand Challenge database show that the Equal Error Rate can be reduced from 0.88% for the GMM technique to 0.36% for the best HMM approach.