Single image subspace for face recognition

Authors:
Jun Liu;Songcan Chen;Zhi-Hua Zhou;Xiaoyang Tan
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, China;National Key Laboratory for Novel Software Technology, Nanjing University, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, China
Venue:
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Year:
2007

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Abstract

Small sample size and severe facial variation are two challenging problems for face recognition. In this paper, we propose the SIS (Single Image Subspace) approach to address these two problems. To deal with the former one, we represent each single image as a subspace spanned by its synthesized (shifted) samples, and employ a newly designed subspace distance metric to measure the distance of subspaces. To deal with the latter one, we divide a face image into several regions, compute the contribution scores of the training samples based on the extracted subspaces in each region, and aggregate the scores of all the regions to yield the ultimate recognition result. Experiments on well-known face databases such as AR, Extended YALE and FERET show that the proposed approach outperforms some renowned methods not only in the scenario of one training sample per person, but also in the scenario of multiple training samples per person with significant facial variations.