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

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

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,提出了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。

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 2 D 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.