title: Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.
abstract: In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.
authors.affiliation: Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
authors.forename: Guangwei
authors.initials: G
authors.lastname: Gao
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
authors.forename: Xiaoyuan
authors.initials: X
authors.lastname: Jing
authors.affiliation: School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
authors.forename: Pu
authors.initials: P
authors.lastname: Huang
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
authors.forename: Juliang
authors.initials: J
authors.lastname: Hua
authors.affiliation: Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Yue
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D005145:Face; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors