Advertisement

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)

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and Machine Recognition of Faces: A Survey. Proceedings of the IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R., Phillips, J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)Google Scholar
  3. 3.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)Google Scholar
  4. 4.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE PAMI 19(7), 711–720 (1997)Google Scholar
  5. 5.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial-frequency and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A: Optics Image Science and Vision 2(7), 1160–1169 (1985)CrossRefGoogle Scholar
  6. 6.
    Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R.P., Konen, W.: Distortion invariant object recognition in the Dynamic Link Architecture. IEEE Transactions on Computers 42(3), 300–311 (1993)CrossRefGoogle Scholar
  7. 7.
    Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)CrossRefGoogle Scholar
  8. 8.
    Shen, L., Bai, L.: A review on Gabor wavelets for face recognition. Pattern Analysis & Applications 9(2-3), 273–292 (2006)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Chung, K.-C., Kee, S.C., Kim, S.R.: Face Recognition using Principal Component Analysis of Gabor Filter Responses. In: International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, pp. 53–57 (1999)Google Scholar
  10. 10.
    Liu, C.J., Wechsler, H.: Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  11. 11.
    Shen, L., Bai, L.: Face recognition based on Gabor features using kernel methods. In: 6th IEEE Conference on Face and Gesture Recognition, pp. 170–175 (2004)Google Scholar
  12. 12.
    Gabor wavelets and general discriminant analysis for face identification and verification. Journal of Image and Vision Computing 27, 1758–1767 (2006) Google Scholar
  13. 13.
    Qin, J., He, Z.-S.: A SVM face recognition method based on Gabor-featured key points. In: Yeung, D.S., Liu, Z.-Q., Wang, X.-Z., Yan, H. (eds.) ICMLC 2005. LNCS (LNAI), vol. 3930, pp. 5144–5149. Springer, Heidelberg (2006)Google Scholar
  14. 14.
    Gilbert, C., Bakan, P.: Visual Asymmetry in Perception of Faces. Neuropsychologia 11(3), 355–362 (1973)CrossRefGoogle Scholar
  15. 15.
    Rhodes, G.: Perceptual Asymmetries in Face Recognition. Brain and Cognition 4(2), 197–218 (1985)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Mitra, S., Lazar, N.A., Liu, Y.: Understanding the role of facial asymmetry in human face identification. Journal Statistics and Computing 17(1), 57–70 (2007)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Burt, D.M., Perrett, D.I.: Perceptual asymmetries in judgements of facial attractiveness, age, gender, speech and expression. Neuropsychologia 35(5), 685–693 (1997)CrossRefGoogle Scholar
  18. 18.
    Fink, B., Neave, N., Manning, J.T., Grammer, K.: Facial symmetry and judgements of attractiveness, health and personality. Personality And Individual Differences 41(3), 491–499 (2006)CrossRefGoogle Scholar
  19. 19.
    Tjan, B.S., Liu, Z.L.: Symmetry impedes symmetry discrimination. Journal of Vision 5(10), 888–900 (2005)CrossRefGoogle Scholar
  20. 20.
    Brady, N., Campbell, M., Flaherty, M.: Perceptual asymmetries are preserved in memory for highly familiar faces of self and friend. Brain and Cognition 58(3), 334–342 (2005)CrossRefGoogle Scholar
  21. 21.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTS: The Extended M2VTS Database. In: 2nd International Conference on Audio and Video-based Biometric Person Authentication, pp. 72–77 (1999)Google Scholar
  22. 22.
    FRAV2D Database (2004), Freely available from: http://www.frav.es/databases/frav2d/
  23. 23.
    Serrano, Å., Conde, C., de Diego, I.M., Cabello, E., Bai, L., Shen, L.: Parallel Gabor PCA with Fusion of SVM Scores for Face Verification. In: International Conference on Computer Vision Theory and Applications, pp. 149–154 (2007)Google Scholar
  24. 24.
    Serrano, Å., de Diego, I.M., Conde, C., Cabello, E., Bai, L., Shen, L.: Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification. In: Haindl, Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 141–150. Springer, Heidelberg (2007)Google Scholar
  25. 25.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)zbMATHGoogle Scholar

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

Personalised recommendations