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Proceedings Paper

Toward hyperspectral face recognition
Author(s): Stefan A. Robila
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Paper Abstract

Face recognition continues to meet significant challenges in reaching accurate results and still remains one of the activities where humans outperform technology. An attractive approach in improving face identification is provided by the fusion of multiple imaging sources such as visible and infrared images. Hyperspectral data, i.e. images collected over hundreds of narrow contiguous light spectrum intervals 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 the efficiency of hyperspectral face recognition through an in house experiment that collected data in over 120 bands within the visible and near infrared range. The imagery was produced using an off the shelf sensor in both indoors and outdoors with the subjects being photographed from various angles. Further processing included spectra collection and feature extraction. Human matching performance based on spectral properties is discussed.

Paper Details

Date Published: 3 March 2008
PDF: 9 pages
Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68120X (3 March 2008); doi: 10.1117/12.765268
Show Author Affiliations
Stefan A. Robila, Montclair State Univ. (United States)


Published in SPIE Proceedings Vol. 6812:
Image Processing: Algorithms and Systems VI
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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