Parallel gabor PCA with fusion of SVM scores for face verification

Ángel Serrano, Cristina Conde, Isaac Martín De Diego, Enrique Cabello, Li Bai, Linlin Shen

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

Here we present a novel fusion technique for support vector machine (SVM) scores, obtained after a dimension reduction with a principal component analysis algorithm (PCA) for Gabor features applied to face verification. A total of 40 wavelets (5 frequencies, 8 orientations) have been convolved with public domain FRAV2D face database (109 subjects), with 4 frontal images with neutral expression per person for the SVM training and 4 different kinds of tests, each with 4 images per person, considering frontal views with neutral expression, gestures, occlusions and changes of illumination. Each set of wavelet-convolved images is considered in parallel or independently for the PCA and the SVM classification. A final fusion is performed taking into account all the SVM scores for the 40 wavelets. The proposed algorithm improves the Equal Error Rate for the occlusion experiment compared to a Downsampled Gabor PCA method and obtains similar EERs in the other experiments with fewer coefficients after the PCA dimension reduction stage.

Original languageEnglish
Pages149-154
Number of pages6
Publication statusPublished - 2007
Externally publishedYes
Event2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 - Barcelona, Spain
Duration: 8 Mar 200711 Mar 2007

Conference

Conference2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007
Country/TerritorySpain
CityBarcelona
Period8/03/0711/03/07

Keywords

  • Biometrics
  • Data fusion
  • Face database
  • Face verification
  • Gabor wavelet
  • Principal component analysis
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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