Influence of wavelet frequency and orientation in an SVM-based parallel Gabor PCA face verification system

Authors:
Ángel Serrano;Isaac Martín de Diego;Cristina Conde;Enrique Cabello;Linlin Shen;Li Bai
Affiliations:
Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Madrid, Spain;Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Madrid, Spain;Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Madrid, Spain;Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Madrid, Spain;Faculty of Information and Engineering, Shenzhen University, Shenzhen, China;School of Computer Science and IT, University of Nottingham, Nottingham, United Kingdom
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

We present a face verification system using Parallel Gabor Principal Component Analysis (PGPCA) and fusion of Support Vector Machines (SVM) scores. The algorithm has been tested on two databases: XM2VTS (frontal images with frontal or lateral illumination) and FRAV2D (frontal images with diffuse or zenithal illumination, varying poses and occlusions). Our method outperforms others when fewer PCA coefficients are kept. It also has the lowest equal error rate (EER) in experiments using frontal images with occlusions. We have also studied the influence of wavelet frequency and orientation on the EER in a one-Gabor PCA. The high frequency wavelets are able to extract more discriminant information compared to the low frequency wavelets. Moreover, as a general rule, oblique wavelets produce a lower EER compared to horizontal or vertical wavelets. Results also suggest that the optimal wavelet orientation coincides with the illumination gradient.