Multi-illumination face recognition from a single training image per person with sparse representation

Authors:
Die Hu;Li Song;Cheng Zhi
Affiliations:
Institute of Image Communication and Information Processing, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Communication and Information Processing, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Communication and Information Processing, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Year:
2010

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Abstract

In real-world face recognition systems, traditional face recognition algorithms often fail in the case of insufficient training samples. Recently, the face recognition algorithms of sparse representation have achieved promising results even in the presence of corruption or occlusion. However a large over-complete and elaborately designed discriminant training set is still required to form sparse representation, which seems impractical in the single training image per person problems. In this paper, we extend Sparse Representation Classification (SRC) to the one sample per person problem. We address this problem under variant lighting conditions by introducing relighting methods to generate virtual faces. Our diverse and complete training set can be well composed, which makes SRC more general. Moreover, we verify the recognition under different lighting environments by a cross-database comparison.