Kappa Weighted Subspace Fusion Approach of Facial Expression Recognition

To improve expression recognition rates,Kappa-based contribution degree computing approach of face sub-area in recognizing expression is proposed. It is utilized as the basis of deriving weights to fuse the subspace prediction results. The normalized face emotion images are partitioned averagely into two sub-regions to obtain the three expression subspaces containing the upper and lower half face parts and the whole face. Each part is represented with Gabor feature. Then,three classifiers: SMO,MLP and KNN are separately used. The expression prediction results are counted to obtain the Kappa. Experiments are done on the CMU and JAFFE two expression image databases and results show that Kappa weighted fusion expression recognition approach has higher recognition accuracy.