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MCS
2007
Springer

Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification

9 years 4 months ago
Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification
In this paper we present a fusion technique for Support Vector Machine (SVM) scores, obtained after a dimension reduction with Bilateralprojection-based Two-Dimensional Principal Component Analysis (B2DPCA) for Gabor features. We apply this new algorithm to face verification. Several experiments have been performed with the public domain FRAV2D face database (109 subjects). A total of 40 wavelets (5 frequencies and 8 orientations) have been used. Each set of wavelet-convolved images is considered in parallel for the B2DPCA and the SVM classification. A final fusion is performed combining the SVM scores for the 40 wavelets with a raw average. The proposed algorithm outperforms the standard dimension reduction techniques, such as Principal Component Analysis (PCA) and B2DPCA.
Ángel Serrano, Isaac Martín de Diego
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where MCS
Authors Ángel Serrano, Isaac Martín de Diego, Cristina Conde, Enrique Cabello, Li Bai, LinLin Shen
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