Feature distribution modelling techniques for 3D face verification

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.
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Pattern Recognition Letters, Elsevier, 2010, 31 (11), pp.1324--1330. 〈10.1016/j.patrec.2010.01.029〉
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Contributeur : Team Perception <>
Soumis le : mardi 3 mai 2011 - 09:51:49
Dernière modification le : lundi 9 mai 2011 - 16:14:37

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Chris Mccool, Jordi Sanchez-Riera, Sébastien Marcel. Feature distribution modelling techniques for 3D face verification. Pattern Recognition Letters, Elsevier, 2010, 31 (11), pp.1324--1330. 〈10.1016/j.patrec.2010.01.029〉. 〈inria-00590261〉

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