Facial expression analysis based on Gabor features and Gaussian Mixture Model
LU Ji-li · Computer Engineering and Applications Journal · 2008
This paper analyzes the nature of the facial expressions and proposes a new facial expression analysis method using Gaussian Mixture Model(GMM) based on Gabor wavelet and principal component analysis.First,the GMM parameters of each facial expression are formed.The probability of every input facial image belongs to one facial expression class can be computed based on the GMM.Then the expression analysis and recognition of facial images is achieved.The experiments on a facial expression database JAFFE have been conducted.The distribution of different expressions is analyzed,and the qualitative and quantitative description of the facial expression is accomplished.The experimental results show that the facial expression analysis method proposed in this paper can rationally represent the daily facial expressions.