Volume 32 Issue 06
Jun.  2006
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Xue Bindang, Xue Wenfang, Jiang Zhiguoet al. Segmental training scheme for embedded hidden markov model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(06): 695-699. (in Chinese)
Citation: Xue Bindang, Xue Wenfang, Jiang Zhiguoet al. Segmental training scheme for embedded hidden markov model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(06): 695-699. (in Chinese)

Segmental training scheme for embedded hidden markov model

  • Received Date: 07 Jul 2005
  • Publish Date: 30 Jun 2006
  • A segmental scheme to retrain E-HMM(embedded hidden Markov models) for face recognition was presented. The current samples were assumed to be a subset of the whole training samples, after the training process, the E-HMM parameters and the necessary temporary parameters in the parameter re-estimating process were saved for the use of next step. When new training samples were added, the trained E-HMM parameters were chosen as the initial parameters, the E-HMM was retrained based on the new samples and the new temporary parameters were obtained. These temporary parameters were combined with the saved temporary parameters to form the final E-HMM parameters so that one person face was presented. Experiments on face database showed that the segmental training method was effective.

     

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  • [1] Rabiner L. A tutorial on HMM and selected applications in speech recognition[J]. Proc of IEEE, 1989,77(2):257-286 [2] Kuo S, Agazzi O. Keyword spotting in poorly printed documents using pseudo 2-D hidden Markov models[J]. IEEE Trans on PAMI, 1994, 16(8):842-848 [3] Nefian A V, Hayes III H. Maximum likelihood training of the embedded HMM for face detection and recognition Proc of the International Conference on Image Processing. Vancouver, BC, Canada:,2000:33-36 [4] Wallhoff F, Eickeler S, Gigoll G. A comparison of discrete and continuous output modeling techniques for a pseudo-2D hidden Markov model face recognition system Proc of International Conference on Image Processing. Thessaloniki. Greece:,2001:685-688 [5] Liu Xiaoming, Chen Tsuhan. Video-based face recognition using adaptive hidden Markov models Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, Wisconsin, USA:,2003:340-345 [6] Zhao W. Face recognition:a literature survey . CS-TR-4167R, 2002 [7] AT&T Laboratories Cambridge. ORL Face database . http://www.uk.research.att.com/facedatabase.html,1994-04-01 [8] Turk M, Pentland A. Eigenceface for recognition[J]. Journal of Cognitive Neuoscience,1991,3(3):71-86 [9] Ben-Arie J, Nandy D. A volumetric/iconic frequency domain representation for object with application for pose invariant face recognition[J]. IEEE Trans on PAMI, 1998,20(3):449-457 [10] Lawrence L, Giles S C, Tsoi A C, et al. Face recognition:a convolutional neural network approach[J]. IEEE Trans on NN, 1997, 8(1):98-113
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