基于稀疏表示的遮挡人脸表情识别方法

摘要

用基于稀疏表示的分类方法识别遮挡人脸表情时，遮挡字典不具有冗余度且身份特征易干扰表情分类。针对此问题，文中提出一种基于稀疏表示的遮挡人脸表情识别方法。该方法首先通过对图像多级分块得到具有冗余度的遮挡字典，然后通过稀疏分解求出待测图像的稀疏表示系数，最后在待测图像所在的子空间内实现表情类别判断。该方法使待测图像的分解系数变得更稀疏，同时避免身份特征对表情分类的干扰。在 Cohn-Kanade 和JAFFE人脸库上的遮挡表情识别实验表明，该方法对遮挡人脸的表情识别具有较强的鲁棒性。%Occlusion dictionary does not have redundancy and facial expression classification is easily disturbed by identity features, which sparse representation based classification( SRC) is used to recognize occluded facial expression. A method for occluded facial expression recognition is proposed to solve this problem. Firstly, an occlusion dictionary with redundancy is constructed by multilevel blocking of the image. Next, sparse representation coefficients of the test image are gained by spare decomposition. Finally, the expression category of test image is judged in its individual subspace. The proposed method makes decomposition coefficients of the test image sparser and avoids identity feature interference to expression classification. The experimental results on Cohn-Kanade and JAFFE face databases show that the proposed method is robust to occluded facial expression recognition.