Improved parameters estimating scheme for E-HMM with application to face recognition

Authors:
Bindang Xue;Wenfang Xue;Zhiguo Jiang
Affiliations:
Image processing center, Beihang University, Beijng, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Image processing center, Beihang University, Beijng, China
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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Pattern Analysis & Applications

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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.