16 February 2006 Face recognition based on HMM in compressed domain
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Abstract
In this paper we present an approach for face recognition based on Hidden Markov Model (HMM) in compressed domain. Each individual is regarded as an HMM which consists of several face images. A set of DCT coefficients as observation vectors obtained from original images by a window are clustered by K-means method using to be the feature of face images. These classified features are applied to train HMMs, so as to get the parameters of systems. Based on the proposed method, both Yale face database and ORL face database are tested. Compared to the other methods relevant to HMM methods reported so far on the two face databases, experimental results by proposed method have shown a better recognition rate and lower computational complexity cost.
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Huiqin Wang, Huiqin Wang, Guocan Feng, Guocan Feng, } "Face recognition based on HMM in compressed domain", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641P (16 February 2006); doi: 10.1117/12.642788; https://doi.org/10.1117/12.642788
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