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Feature distribution modelling techniques for 3D face verification
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DOI：10.1016/j.patrec.2010.01.029.png)
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Abstract
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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. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Feature distribution modelling
Hidden Markov models
Gaussian mixture models
3D face verification
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