基于低秩表示中稀疏误差的可变光照和局部遮挡人脸识别

可变光照和有遮挡人脸识别是人脸识别问题中的一个难点。受到鲁棒主成分分析法(RPCA)和稀疏表示分类法(SRC)的启发,提出一种基于低秩表示(LRR)中稀疏误差图像的可变光照有遮挡人脸识别算法。在训练阶段,利用LRR计算每类人脸低秩数据矩阵,在此基础上求解每类人脸图像低秩映射矩阵,通过各类低秩映射矩阵将未知人脸图像投影得到每类下的低秩数据矩阵和稀疏误差矩阵,为了有效提取稀疏误差图像中的鉴别信息,分别对稀疏误差图像进行边缘检测和平滑度分析,设计了基于两者加权和的类别判据。在Extended Yale B和AR两个数据库上进行了详细的实验分析,实验结果与其它算法相比较有明显提高,证实了所提算法的有效性和鲁棒性。

Face recognition with varying illumination and occlusion based on sparse error of low rank representation

Although difficult to deal with, face recognition with varying illumination and occlusion has been widely investigated in recent years. Motivated by the popular methods of robust principal component analysis (RPCA) and sparse representation based classification (SRC), we present a novel algorithm which uses the sparse error of low rank representation (LRR) for face recognition with varying illumination and occlusion. As for each type of training samples, we first calculate their low rank matrix using LRR and then construct low rank projection between the training face matrix and the obtained low rank data matrix. With the constructed low rank projection, any test face image can obtain a low rank matrix and a sparse error matrix corresponding on each face category. In order to fully extract the discrimination information of the sparse error image, its smoothness and edge information are analyzed respectively. Furthermore, a set of concrete classification criteria is proposed, which fuses smoothness information with the edge information using weighted sum rules. Experiment results on face databases of AR and the Extended Yale B confirm that the proposed method is robust to varying illumination and occlusion, and has better recognition rate than many other methods.