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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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https://hal.inria.fr/inria-00590261
Contributor : Team Perception <>
Submitted on : Tuesday, May 3, 2011 - 9:51:49 AM
Last modification on : Thursday, February 7, 2019 - 5:55:48 PM

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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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