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Proceedings Paper

Illumination-invariant face recognition in hyperspectral images
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Paper Abstract

We examine the performance of illumination-invariant face recognition in hyperspectral images on a database of 200 subjects. The images are acquired over the near-infrared spectral range of 0.7-1.0 microns. Each subject is imaged over a range of facial orientations and expressions. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional linear subspaces of reflected radiance spectra. One hundred outdoor illumination spectra measured at Boulder, Colorado are used to synthesize the radiance spectra for the face tissue types. Weighted invariant subspace projection over multiple tissue types is used for recognition. Illumination-invariant face recognition is tested for various face rotations as well as different facial expressions.

Paper Details

Date Published: 23 September 2003
PDF: 8 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.488561
Show Author Affiliations
Zhihong Pan, Univ. of California, Irvine (United States)
Glenn E. Healey, Univ. of California, Irvine (United States)
Manish Prasad, Univ. of California, Irvine (United States)
Bruce J. Tromberg, Beckman Laser Institute (United States)


Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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