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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zihan Zhou Ganesh, A. Wright, J. Shen-Fu Tsai Yi Ma |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL (Zihan Zhou; Ganesh, A.; Wright, J.; Shen-Fu Tsai; Yi Ma) |
| Abstract | We consider the problem of recognizing human faces despite variations in both pose and illumination, using only frontal training images. We propose a very simple algorithm, called nearest-subspace patch matching, which combines a local translational model for deformation due to pose with a linear subspace model for lighting variations. This algorithm gives surprisingly competitive performance for moderate variations in both pose and illumination, a domain that encompasses most face recognition applications, such as access control. The results also provide a baseline for justifying the use of more complicated face models or more advanced learning methods to handle more extreme situations. Extensive experiments on publicly available databases verify the efficacy of the proposed method and clarify its operating range. |
| Sponsorship | City of Amsterdam |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 512557 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424421534 |
| DOI | 10.1109/AFGR.2008.4813452 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-09-17 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition Lighting Testing Image recognition Deformable models Shape Face detection Humans Access control Learning systems |
| Content Type | Text |
| Resource Type | Article |
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