Facial expression recognition based on two dimensional feature extraction

In this paper, approaches to facial expression recognition based on two dimension methods are studied. 2D expression feature extraction methods include 2DPCA, 2DLDA and generalized low rank approximation of matrices (GLRAM) which are usually used in face recognition or data compression. After expression features are extracted, support vector machine classifier is used for expression classification. Extensive expression recognition experiments are carried out on Japanese female facial expression database (JAFFE) to study the influence of feature dimension on recognition ratio and the results are also compared with that of the tradition 1D feature extraction methods such as PCA and LDA. Experiment results show that 2D methods are effective in expression recognition application and usually outperform 1D methods.