Quo vadis face recognition: Spectral considerations

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

1 Scopus citations

Abstract

The paper provides novel approaches for the employment of spectral information when pursuing face recognition. We designed and tested Eigenface based algorithms that improve face recognition through feature extraction, i.e. extracting the 'best bands' according to various criteria such as decorelation and statistical independence. Eigenfaces correspond to principal components and have previously been used for regular grayscale and color images. In this paper we expand theiruse to hyperspectral imagery, i.e. data sets of images of the same scene associated to narrow wavelength intervals. Our approach is a two decomposition process. In the first, the hyperspectral data is reduced to grayscale using Principal Component Analysis. In the second, the grayscale images are processed using the classical Eigenface detection algorithm. The results suggest that spectral imaging improves face classification over its counterpart.

Original languageEnglish
Title of host publication2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009
DOIs
StatePublished - 25 Sep 2009
Event2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009 - Farmingdale, NY, United States
Duration: 1 May 20091 May 2009

Publication series

Name2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009

Other

Other2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009
CountryUnited States
CityFarmingdale, NY
Period1/05/091/05/09

Keywords

  • Hyperspectral imaging
  • Image classification
  • Principal component analysis
  • Spectral analysis

Fingerprint Dive into the research topics of 'Quo vadis face recognition: Spectral considerations'. Together they form a unique fingerprint.

  • Cite this

    Robila, S. A. (2009). Quo vadis face recognition: Spectral considerations. In 2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009 [5031555] (2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009). https://doi.org/10.1109/LISAT.2009.5031555