Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24023
Title: Efficient misalignment-robust representation for real-time face recognition
Authors: Yang, M
Zhang, L 
Zhang, D 
Issue Date: 2012
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2012, v. 7572 LNCS, no. PART 1, p. 850-863 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Sparse representation techniques for robust face recognition have been widely studied in the past several years. Recently face recognition with simultaneous misalignment, occlusion and other variations has achieved interesting results via robust alignment by sparse representation (RASR). In RASR, the best alignment of a testing sample is sought subject by subject in the database. However, such an exhaustive search strategy can make the time complexity of RASR prohibitive in large-scale face databases. In this paper, we propose a novel scheme, namely misalignment robust representation (MRR), by representing the misaligned testing sample in the transformed face space spanned by all subjects. The MRR seeks the best alignment via a two-step optimization with a coarse-to-fine search strategy, which needs only two deformation-recovery operations. Extensive experiments on representative face databases show that MRR has almost the same accuracy as RASR in various face recognition and verification tasks but it runs tens to hundreds of times faster than RASR. The running time of MRR is less than 1 second in the large-scale Multi-PIE face database, demonstrating its great potential for real-time face recognition.
Description: 12th European Conference on Computer Vision, ECCV 2012, Florence, 7-13 October 2012
URI: http://hdl.handle.net/10397/24023
ISBN: 9783642337178
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-33718-5_61
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