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Proceedings Paper

Investigating face recognition from hyperspectral data: impact of band extraction
Author(s): Stefan A. Robila; Andrew LaChance; Shawna Ruff
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Paper Abstract

Among various biometrics measures used in human identification, face recognition, has the distinct advantage of not requiring the subjects collaboration. Hyperspectral data constitute a natural choice for expanding face recognition image fusion, especially since it may provide information beyond the normal visible range, thus exceeding the normal human sensing. In this paper we investigate algorithms that improve face recognition by extracting the 'best bands' according to various criteria such as decorrelation and statistical independence. The work expands on previous band extraction results and has the distinct advantage of being one of the first that combines spatial information (i.e. face characteristics) with spectral information.

Paper Details

Date Published: 27 April 2009
PDF: 10 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341Y (27 April 2009); doi: 10.1117/12.817025
Show Author Affiliations
Stefan A. Robila, Montclair State Univ. (United States)
Andrew LaChance, Appalachian State Univ. (United States)
Shawna Ruff, Gonzaga Univ. (United States)

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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