Sparse representation-based face recognition for one training image per person

Authors:
Xueping Chang;Zhonglong Zheng;Xiaohui Duan;Chenmao Xie
Affiliations:
Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

In this paper, motivated by the recent development of sparse representation (SR) and compressive sensing (CS), in order to address one sample problem, we propose two approaches: shifted images +SRC (SSRC) and reconstructed images +SRC (RSRC). Specifically, we generate the multiple images by shifting the original image or reconstructing the original image via PCA(Principle Component Analysis), and regard new images as training samples, and then apply SRC (Sparse Representation-based Classification) on new training samples set. The experimental results on the two popular face databases (ORL and Yale) demonstrate the feasibility and effectiveness of our proposed methods.