Influence of Wavelet Frequency and Orientation in an SVM-Based Parallel Gabor PCA Face Verification System

  • Ángel Serrano
  • Isaac Martín de Diego
  • Cristina Conde
  • Enrique Cabello
  • Linlin Shen
  • Li Bai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


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.


Face Verification Gabor Wavelet Parallel Gabor Principal Component Analysis Support Vector Machine Data Fusion 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ángel Serrano
    • 1
  • Isaac Martín de Diego
    • 1
  • Cristina Conde
    • 1
  • Enrique Cabello
    • 1
  • Linlin Shen
    • 2
  • Li Bai
    • 3
  1. 1.Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Camino del Molino s/n, Fuenlabrada E-28943 (Madrid)Spain
  2. 2.Faculty of Information and Engineering, Shenzhen University, Shenzhen, 518060China
  3. 3.School of Computer Science and IT, University of Nottingham, Nottingham, NG8 1BBUnited Kingdom

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