Learning-based image representation and method for face recognition

Zhiming Liu, Chengjun Liu, Qingchuan Tao

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

2 Scopus citations

Abstract

This paper presents a novel method for face recognition. First, we generate the new image representation from the decorrelated hybrid color configurations rather than RGB color space via a learning algorithm. The learning algorithm, Principal Component Analysis (PCA) plus Fisher Linear Discriminant analysis (FLD), is able to derive the desired color transformation to generate a discriminating image representation that is optimal for face recognition. Second, we partition face image into some small patches, each of which can obtain its own color transformation, to reduce the effect of illumination variations. Thus, a patch-based novel image representation method is proposed for face recognition. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that the proposed method outperforms gray-scale image and some recent methods in face recognition.

Original languageEnglish (US)
Title of host publicationIEEE 3rd International Conference on Biometrics
Subtitle of host publicationTheory, Applications and Systems, BTAS 2009
DOIs
StatePublished - Dec 16 2009
EventIEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009 - Washington, DC, United States
Duration: Sep 28 2009Sep 30 2009

Publication series

NameIEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

Other

OtherIEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009
CountryUnited States
CityWashington, DC
Period9/28/099/30/09

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

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