Unified model in identity subspace for face recognition

Authors:
Pin Liao;Li Shen;Yi-Qiang Chen;Shu-Chang Liu
Affiliations:
Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100080, P.R. China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100080, P.R. China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100080, P.R. China;Multimedia Information Technology Teaching Center, School of Information, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China
Venue:
Journal of Computer Science and Technology - Special issue on computer graphics and computer-aided design
Year:
2004

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Abstract

Human faces have two important characteristics: (1) They are similar objects and the specific variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information, is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people group A, can be generalized well to facial images of unknown individuals, and can be used to recognize facial images from an unknown people group B with only one sample per subject. Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.