Investigating face recognition from hyperspectral data: impact of band extraction

Stefan A. Robila; Andrew LaChance; Shawna Ruff

doi:10.1117/12.817025

27 April 2009 Investigating face recognition from hyperspectral data: impact of band extraction

Stefan A. Robila, Andrew LaChance, Shawna Ruff

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73341Y (2009) https://doi.org/10.1117/12.817025
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Stefan A. Robila, Andrew LaChance, and Shawna Ruff "Investigating face recognition from hyperspectral data: impact of band extraction", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341Y (27 April 2009); https://doi.org/10.1117/12.817025

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