Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition

Abstract

Extracting effective features of expressions becomes a hot research topic, and a single feature pattern cannot reflect the diversity of expressions. Therefore, to obtain rich information feature data and raise the expression recognition performance, we propose a feature fusion model of multiple feature selection by the measure of the RV correlation coefficient. In the proposed feature fusion model, the feature patterns are firstly selected by RV correlation coefficient from various expression texture features. And then according to rank the values of the RV correlation coefficient, we build a CCA subspace and PCA subspace respectively to fuse selected features. Finally, a new facial expression feature presentation is constructed through weighting and combining the two fusion features from the subspaces. The new features are fed to SVM classifier for expression recognition. Experimental verification shows that our proposed model has a superior performance than the existing algorithms.