Expression recognition based on EPCA and SVM

Equable principal component analysis (EPCA) is a powerful technique of feature extracting. It can reduce a large set of correlated variables to a smaller number of uncorrelated components. Support vector machines (SVM) is a novel pattern classification approach. It is very efficient in solving clustering problems that are not linearly separable. This paper presents a method of expression recognition based on the EPCA and SVM. According to the EPCA extracting feature, this paper recognizes expression with SVM. The multi-class classification problem is solved by the approach of one-against all SVM classifier. Experiments of human who participates in test have been trained or not are performed on the JAFFE and Yale database. And compared to the nearest classifier, the EPCA and SVM can get better recognition ratio. Therefore, it is feasible to apply EPCA and SVM to expression recognition.