Journal:IEEE International Conference on Image Processing (ICIP)

Key Words:low-rank representation, sparse representation, robust PCA, feature extraction, face recognition

Abstract: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.

Co-author:Shuting Cai,Junbin Gao

First Author:Ming Yin

Indexed by:会议论文

Page Number:2013, p: 3770-3774. (CCF C类会议)

Translation or Not:no

Date of Publication:2013-09-13