An HMM Based Normality Processing Method without Overflow for High Dimensional Sample Set
Tang Jing · 2008
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.