Investigating face recognition from hyperspectral data: Impact of band extraction

Stefan A. Robila, Andrew LaChance, Shawna Ruff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Volume7334
DOIs
StatePublished - 14 Sep 2009
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV - Orlando, FL, United States
Duration: 13 Apr 200916 Apr 2009

Other

OtherAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
CountryUnited States
CityOrlando, FL
Period13/04/0916/04/09

Keywords

  • Face recognition
  • Feature extraction
  • Hyperspectral data

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    Robila, S. A., LaChance, A., & Ruff, S. (2009). Investigating face recognition from hyperspectral data: Impact of band extraction. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV (Vol. 7334). [73341Y] https://doi.org/10.1117/12.817025