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Improved Parameters Estimating Scheme for E-HMM with Application to Face Recognition

  • Bindang Xue
  • Wenfang Xue
  • Zhiguo Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

This paper presents a new scheme to initialize and re-estimate Embedded Hidden Markov Models(E-HMM) parameters for face recognition. Firstly, 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 possible retraining use. When new training samples were added to the training samples, the saved E-HMM parameters were chosen as the initial model parameter. Then the E-HMM was retrained based on the new samples and the new temporary parameters were obtained. Finally, these temporary parameters were combined with saved temporary parameters to form the final E-HMM parameters for representing one person face. Experiments on ORL databases show the improved method is effective.

Keywords

Training Sample Hide Markov Model Face Recognition Face Image State Probability Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bindang Xue
    • 1
  • Wenfang Xue
    • 2
  • Zhiguo Jiang
    • 1
  1. 1.Image processing centerBeihang UniversityBeijngChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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