Design of Recognition for Facial Expression by Hidden Markov Model

Hidden Markov Model(HMM) is a widely used statistical model.This paper deal with the design of a recognition system for facial expression in the light of the principal theory and algorithm of the HMM.The system consists of two levels of HMM,with the low level composed of six HMM,corresponding to six expression-specific.The vectors of the facial expression features,after put into the system,is primarily identified through the low HMMsand result in the observation vectors of the high level of the HMM.Through deciphering of the HMM,the expression are identified,thus enhancing the recognition rate and strengthening the system to a higher level.