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| An HMM Based Normality Processing Method without Overflow for High Dimensional Sample Set |
| TANG Jing-Hai1, ZHANG You-Wei1,2 |
1.School of Electronics and Information Engineering, Beihang University, Beijing 1000832.
School of Information, Wuyi University, Jiangmen 529020 |
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Abstract Aiming at the overflow of the hidden Markov model (HMM) observation probability, a method is proposed, called Normality Processing. Firstly, the chi-square plot is used to test normality of the sample set, the transformation of square root is performed. The feasibility of the proposed method is validated on the expression sequences database of CED-WYU(1.0) and Cohn-Kanade (CMU). The person-independent expression recognition experiment is made with continuous HMM based on the optical flow features and a better result is obtained when the normality processing is used.
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Received: 31 January 2007
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2008, Vol. 21 
