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From few to many: illumination cone models for face recognition under variable lighting and pose
Abstract
We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.
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Cited by
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A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
“Many areas of computer vision, such as stereo (Scharstein and Szeliski 2002), face recognition (Philips et al. 2005; Sim et al. 2003; Gross et al. 2008; Georghiades et al. 2001), and object recognition (Fei-Fei et al.”
BackgroundThe CAS-PEAL large-scale chinese face database and baseline evaluations
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., & Zhao, D. (2008) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2007.909557 · Cited by 773
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
Fast Face-Swap Using Convolutional Neural Networks
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Metadata
- Authors
- Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2001-06
- Volume
- 23
- Issue
- 6
- Pages
- 643-660
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2123921160
- Cited by
- 4,912
- References
- 2
- FWCI
- 52.10
- Type
- journal article
- Language
- en
- Open access
- closed
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