Gabor Surface Feature for face recognition

Ke Yan, Youbin Chen, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

25 Citations (Scopus)


Gabor filters can extract multi-orientation and multiscale features from face images. Researchers have designed different ways to use the magnitude of the filtered results for face recognition: Gabor Fisher classifier exploited only the magnitude information of Gabor magnitude pictures (GMPs); Local Gabor Binary Pattern uses only the gradient information. In this paper, we regard GMPs as smooth surfaces. By completely describing the shape of GMPs, we get a face representation method called Gabor Surface Feature (GSF). First, we compute the magnitude, 1stand 2ndderivatives of GMPs, then binarize them and transform them into decimal values. Finally we construct joint histograms and use subspace methods for classification. Experiments on FERET, ORL and FRGC 1.0.4 database show the effectiveness of GSF.
Original languageEnglish
Title of host publication1st Asian Conference on Pattern Recognition, ACPR 2011
Number of pages5
Publication statusPublished - 1 Dec 2011
Event1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
Duration: 28 Nov 201128 Nov 2011


Conference1st Asian Conference on Pattern Recognition, ACPR 2011


  • face recognition
  • feature extraction
  • Gabor
  • Gabor surface feature
  • histogram

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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