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发表刊物：IEEE International Conference on Image Processing (ICIP)

关键字：low-rank representation, sparse representation, robust PCA, feature extraction, face recognition

摘要：Feature extraction is one of the most fundamental problems in face recognition tasks. In this paper, motivated by low-rank representation (LRR) model on exploring the multiple subspace structures of observation data, we propose a double low-rank matrix recovery method to learn low-rank subspaces from face images, where it takes into account the recovery of row space and column space information simultaneously. Applying Augmented Lagrangian Multiplier (ALM), the optimization problem on minimization of nuclear norm is resolved efficiently. By evaluating on public face databases, experimental results show that our proposed method works much better than existing face recognition methods based on feature extraction. It is more robust to outliers, varying illumination and occlusion.

合写作者：Shuting Cai,Junbin Gao

第一作者：Ming Yin

论文类型：会议论文

页面范围：2013, p: 3770-3774. (CCF C类会议)

是否译文：否

发表时间：2013-09-13