23 September 2003 Illumination-invariant face recognition in hyperspectral images
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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.
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Zhihong Pan, Zhihong Pan, Glenn E. Healey, Glenn E. Healey, Manish Prasad, Manish Prasad, Bruce J. Tromberg, Bruce J. Tromberg, } "Illumination-invariant face recognition in hyperspectral images", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.488561; https://doi.org/10.1117/12.488561
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