基于压缩感知的鲁棒性人脸表情识别

为了有效提高噪声背景下的人脸表情识别性能,提出一种基于压缩感知的鲁棒性人脸表情识别方法.先通过对腐蚀的测试样本表情图像进行稀疏表示,再利用压缩感知理论寻求其最稀疏的解,然后采用求得的最稀疏解信息实现人脸表情的分类.在标准的Cohn-Kanade表情数据库的实验测试结果表明,该方法取得的人脸表情识别性能优于最近邻法、支持向量机以及最近邻子空间法.可见,该方法用于人脸表情识别,识别效果较好,鲁棒性较高.

Compressed Sensing-Based Robust Facial Expression Recognition

In order to effectively improve the performance of facial expression recognition under the noisy background, a method of robust facial expression recognition based on compressed sensing is proposed. Firstly, the sparse representation of corrupted expression images of the identified test sample is sought, then the compressed sensing theory is used to solve its sparsest solution. Finally, according to the sparsest solution, facial expression classification is performed. Experimental results on benchmarking Cohn-Kanade database show that facial expression performance obtained by this method is better than the nearest neighbor (NN), support vector machine (SVM) and the nearest subspace (NS). Therefore, the proposed method shows both good recognition performance and high robustness on facial expression recognition tasks.