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Face Recognition Across Pose and Illumination

Ralph Gross, Simon Baker, Iain Matthews, and Takeo Kanade
Book Section/Chapter, Handbook of Face Recognition, pp. 197 - 221, June, 2004
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The last decade has seen automatic face recognition evolve from small scale research systems to a wide range of commercial products. Driven by the FERET face database and evaluation protocol, the currently best commercial systems achieve recognition accuracies comparable to those of fingerprint recognizers. In these experiments however, only frontal face images taken under controlled lighting conditions were used. As the use of face recognition systems expands towards less restricted environments, the development of algorithms for view and illumination invariant face recognition becomes important. However, the performance of current algorithms degrades significantly when tested across pose and illumination as documented in a number of evaluations. In this chapter we review previously proposed algorithms for pose and illumination invariant face recognition. We then describe in detail two successful appearance-based algorithms for face recognition across pose, eigen light-fields and Bayesian face subregions. We furthermore show how both of these algorithms can be extended towards face recognition across pose and illumination.

The research described here was supported by U.S. Office of Naval Research contract N00014-00-1-0915 and in part by U.S. Department of Defense contract N41756-03-C4024. Portions of the research in this paper used the FERET database of facial images collected under the FERET program.


author = {Ralph Gross and Simon Baker and Iain Matthews and Takeo Kanade},
title = {Face Recognition Across Pose and Illumination},
booktitle = {Handbook of Face Recognition},
publisher = {Springer-Verlag},
editor = {Stan Z. Li and Anil K. Jain},
year = {2004},
month = {June},
pages = {197 - 221},

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