Facial Expression Recognition Based on Hybrid Features and Multiple HMMs Fusion for Image Sequences

Most of facial expression recognition methods for image sequences generally extract one kind of features currently,which results in a shortage that the features can not effectively reflect comprehensive facial emotional information.A method of expression recognition based on hybrid features and multiple HMMs fusion for image sequences is presented to address this problem in this paper.Texture features for the eye area are extracted by using Gabor wavelet transformation,texture features for the nose area are extracted by using 2D-DCT,and shape deform features for the mouth area are extracted by using AAM.Discrete HMM is adopted for expression recognition in each expression area of the testing image sequences respectively.The recognition results are fused by means of integrating the probability of each expression in each area with its weight obtained by contribution analysis algorithm,and the final expression is determined as that with the maximal probability.Experimental results show that the method can integrate the texture and shape deform features of expressions and get high recognition rate.The method is highly efficient in its running and is suitable for real time expression recognition.