The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A low-dimensional representation of human faces for arbitrary lighting conditions
Abstract
When recognizing a fixed object from a fixed viewpoint, the dominant source of variation in image intensity is lighting changes. We propose a low-dimensional model for human faces that can both synthesize a face image when given lighting conditions and can estimate lighting conditions when given a face image. The model can handle non-Lambertian and self-shadowing surfaces such as faces because it does not make any assumptions about either the surface geometry or bidirectional reflectance function. The model can be adapted to handle any arbitrary lighting condition, and is easily extendable to any other viewpoint or to any other object.>
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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
Efficient region tracking with parametric models of geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.722606 · Cited by 1,066
Separating Style and Content with Bilinear Models
Tenenbaum, J. B., & Freeman, W. T. (2000) · Neural Computation · doi:10.1162/089976600300015349 · Cited by 840
Metadata
- Authors
- Hallinan
- Venue
- Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94
- Publisher
- IEEE Comput. Soc. Press
- Published
- 1994
- Pages
- 995-999
- OpenAlex
- W2168068730
- Cited by
- 293
- References
- 1
- FWCI
- 9.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hallinan. (1994). A low-dimensional representation of human faces for arbitrary lighting conditions. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94. https://doi.org/10.1109/cvpr.1994.323941
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
