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Proceedings Paper

Multiband and spectral eigenfaces for face recognition in hyperspectral images
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Paper Abstract

Spectral reflectance properties of local facial regions have been shown to be useful discriminants for face recognition. To evaluate the performance of spectral signature methods versus purely spatial methods, face recognition tests are conducted using the eigenface method for single-band images extracted from the hyperspectral images. This is the first such comparison based on the same dataset. Selected sets of bands as well as PCA transformed bands are also used for face recognition evaluation with individual band processed separately. A new spectral eigenface method which preserves both spatial and spectral features is proposed. All algorithms based on spectral and/or spatial features are evaluated under the same framework and are compared in terms of accuracy and computational efficiency.

Paper Details

Date Published: 28 March 2005
PDF: 8 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.603446
Show Author Affiliations
Zhihong Pan, Galileo Group, Inc. (United States)
Glenn E. Healey, Univ. of California/Irvine (United States)
Bruce Tromberg, Beckman Laser Institute (United States)


Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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