Feature distribution modelling techniques for 3D face verification

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Chris McCool, Jordi Sanchez-Riera, Sébastien Marcel

Published: 2010, Last Modified: 05 Mar 2025Pattern Recognit. Lett. 2010EveryoneRevisionsBibTeXCC BY-SA 4.0
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.