Authors:
Ángel Serrano
1
;
Cristina Conde
1
;
Isaac Martín de Diego
1
;
Enrique Cabello
1
;
Li Bai
2
and
Linlin Shen
2
Affiliations:
1
Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Spain
;
2
School of Computer Science & IT, University of Nottingham, United Kingdom
Keyword(s):
Biometrics, face verification, face database, Gabor wavelet, principal component analysis, support vector machine, data fusion.
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 coefficient
s after the PCA dimension reduction stage.
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