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Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization
Pengfei Zhu1, Lei Zhang1, Qinghua Hu2, and Simon C.K. Shiu1
1Biometric Research Center, Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong
cspzhu@comp.polyu.edu.hk
cslzhang@comp.polyu.edu.hk
2School of Computer Science and Technology, Tianjin University, China
Abstract. Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a non-trivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multi-scale patch based CRC method, while the ensemble of multi-scale outputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms.
LNCS 7572, p. 822 ff.
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© Springer-Verlag Berlin Heidelberg 2012