Feature distribution modelling techniques for 3D face recognition

This paper shows that Hidden Markov Models (HMMs) can be effectively ap- plied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian Mixture Model (GMM) technique. Experi- ments 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.


Published in:
Pattern Recognition Letters, 31, 1324-1330
Year:
2010
Keywords:
Laboratories:




 Record created 2011-02-07, last modified 2018-09-13


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