针对混合污染的结构化鲁棒低秩恢复算法在人脸识别中的应用

传统的低秩恢复算法在识别有混合污染的人脸图像时，通常只对污染部分进行一种类型的约束，并不能很好地恢复出干净的样本。针对这种情况，提出结构化鲁棒低秩恢复算法（structured and robust low-rank recovery for mixed contamination，SRLRR）。SRLRR算法利用对二维误差图像的低秩约束移除样本中的连续污染部分，同时利用稀疏约束分离样本中服从拉普拉斯分布的噪声。另外，为了学习到更具有鉴别性的低秩表示，该算法对表示系数进行了块对角结构化约束。在三个常用数据库上的实验证明了SRLRR算法的有效性和鲁棒性。

Structured robust low-rank recovery algorithm for face recognition with mixed contaminations

When there exist mixed contaminations in face images, traditional low-rank recovery algorithms usually imposes only one constraint on the corresponding contaminations, it cannot recover clean samples very well. In order to solve this problem, this paper proposed a structured robust low-rank recovery algorithm(SRLRR). The SRLRR algorithm imposed low-rank constraint on the 2D error image to remove the continuous contamination, and introduced sparse constraint to separate the noise that obeyed the Laplacian distribution in samples. Moreover, the proposed algorithm imposed a block-diagonal structured constraint on the representation coefficient to learn the more discriminative low-rank representation. The experimental results on three commonly and using standard databases verify the effectiveness and robustness of the proposed SRLRR algorithm.