Kappa加权的子空间融合表情识别方法

Kappa Weighted Subspace Fusion Approach of Facial Expression Recognition

为提高面部表情识别效果,提出基于Kappa计算面部表情图像子区域对表情的贡献程度,并线性加权子空间预测结果.将标准化后的人脸表情图像上下平均分割成2个子区域,确定上半脸和下半脸及全脸3个表情子空间,采用Gabor小波特征,分别利用SMO、MLP和KNN三种分类器,统计并计算基于Kappa的子空间表情信息.在Cohn-Kanade和JAFFE两个表情图像库进行测试,实验结果表明:基于Kappa加权融合的表情识别方法识别率更高.

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 subspaees 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.