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- W2357223709 abstract "Embedded Hidden Markov Model (E|HMM) can extract the main features of faces and has necessary robustness property in treating diversities of poses and lighting environments. This paper deals with the performance improvement of E|HMM and its implementation. First, The effects of the size of the sampling window and the terms of 2D|DCT coefficients of every image block on the face recognition accuracy are analyzed, and the optimal sizes of the sampling window and the terms of 2D|DCT coefficients of every image block are selected based on the analysis results. In view of the fact that the contribution entropies of different photos to the final face E|HMM are different, a new weighted synthesis method for re|estimating E|HMM parameters is developed and described in detail. During the period of re|estimating the E|HMM parameters, every training sample was represented by one E|HMM, the model parameters for every sample were obtained firstly, then the different model parameters were synthesized to one model through weighted method, and the weights were adaptively calculated in the training stage. After the training, one person′s face′s images were represented by one E|HMM parameters. The running experimental results with ORL (Olivetti Research Ltd.) face database show that the recognition rate with this new approach has reached about 99.5%." @default.
- W2357223709 created "2016-06-24" @default.
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- W2357223709 date "2002-01-01" @default.
- W2357223709 modified "2023-09-26" @default.
- W2357223709 title "Weighted synthesis embedded-hidden Markov model for face recognition" @default.
- W2357223709 hasPublicationYear "2002" @default.
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