Studies on hyperspectral face recognition in visible spectrum with feature band selection

Wei Di, Lei Zhang, Dapeng Zhang, Quan Pan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

95 Citations (Scopus)

Abstract

This correspondence paper studies face recognition by using hyperspectral imagery in the visible light bands. The spectral measurements over the visible spectrum have different discriminatory information for the task of face identification, and it is found that the absorption bands related to hemoglobin are more discriminative than the other bands. Therefore, feature band selection based on the physical absorption characteristics of face skin is performed, and two feature band subsets are selected. Then, three methods are proposed for hyperspectral face recognition, including whole band (2D)2PCA, single band (2D)2PCA with decision level fusion, and band subset fusion-based (2D)2PCA. A simple yet efficient decision level fusion strategy is also proposed for the latter two methods. To testify the proposed techniques, a hyperspectral face database was established which contains 25 subjects and has 33 bands over the visible light spectrum (0.40.72 μm). The experimental results demonstrated that hyperspectral face recognition with the selected feature bands outperforms that by using a single band, using the whole bands, or, interestingly, using the conventional RGB color bands.
Original languageEnglish
Article number5512681
Pages (from-to)1354-1361
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume40
Issue number6
DOIs
Publication statusPublished - 1 Nov 2010

Keywords

  • Band selection
  • face recognition
  • hyperspectral imaging
  • principal component analysis (PCA)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this