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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Xue, B., Xue, W., & Jiang, Z. (2006). Improved parameters estimating scheme for E-HMM with application to face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3832 LNCS, pp. 199–205). https://doi.org/10.1007/11608288_27
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