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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.
Ali H. Sayed, Mert Kayaalp, Stefan Vlaski, Virginia Bordignon
Ramya Rasipuram, Marzieh Razavi
Pierre Dillenbourg, Bixio Rimoldi, Adrian Dan Tarniceriu