The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
From few to many: generative models for recognition under variable pose and illumination
Abstract
Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-based approaches, require only a small set of training images to generate a rich representation that models this variability. Specifically, from as few as three images of an object in fixed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was specifically gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods.
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Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Cited by
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
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
Ricanek, K., & Tesafaye, T. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.78 · Cited by 778
Metadata
- Authors
- Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J.
- Venue
- Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Pages
- 277-284
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2102760078
- Cited by
- 259
- References
- 1
- FWCI
- 14.88
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J. (2002). From few to many: generative models for recognition under variable pose and illumination. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580). https://doi.org/10.1109/afgr.2000.840647
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