Skip to main content

Information Theory for Gabor Feature Selection for Face Recognition

Abstract

A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

References

  1. 1.

    Daugman JG: 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 1985, 2(7):1160–1169. 10.1364/JOSAA.2.001160

    Article  Google Scholar 

  2. 2.

    Okajima K: Two-dimensional Gabor-type receptive field as derived by mutual information maximization. Neural Networks 1998, 11(3):441–447. 10.1016/S0893-6080(98)00007-0

    Article  Google Scholar 

  3. 3.

    Kyrki V, Kamarainen J-K, Kälviäinen H: Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters 2004, 25(3):311–318. 10.1016/j.patrec.2003.10.008

    Article  Google Scholar 

  4. 4.

    Phillips PJ, Moon H, Rizvi SA, Rauss PJ: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(10):1090–1104. 10.1109/34.879790

    Article  Google Scholar 

  5. 5.

    Wiskott L, Fellous J-M, Kuiger N, von der Malsburg C: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7):775–779. 10.1109/34.598235

    Article  Google Scholar 

  6. 6.

    Messer K, Kittler J, Sadeghi M, et al.: Face authentication test on the BANCA database. Proceedings of 17th International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 4: 523–532.

    Article  Google Scholar 

  7. 7.

    Lades M, Vorbruggen JC, Buhmann J, et al.: Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 1993, 42(3):300–311. 10.1109/12.210173

    Article  Google Scholar 

  8. 8.

    Liu C, Wechsler H: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 2002, 11(4):467–476. 10.1109/TIP.2002.999679

    Article  Google Scholar 

  9. 9.

    Shen L, Bai L: Gabor feature based face recognition using Kernel methods. Proceedings of 6th IEEE International Conference on Automatic Face and Gesture Recognition(FGR '04), May 2004, Seoul, South Korea 170–176.

    Google Scholar 

  10. 10.

    Fasel IR, Bartlett MS, Movellan JR: A comparison of Gabor filter methods for automatic detection of facial landmarks. Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition(FGR '02) , May 2002, Washington, DC, USA 231–235.

    Google Scholar 

  11. 11.

    Liu D-H, Lam K-M, Shen L-S: Optimal sampling of Gabor features for face recognition. Pattern Recognition Letters 2004, 25(2):267–276. 10.1016/j.patrec.2003.10.007

    Article  Google Scholar 

  12. 12.

    Campbell NW, Thomas BT: Automatic selection of Gabor filters for pixel classification. Proceeding of 6th IEE International Conference on Image Processing and Its Applications(IPA '97), July 1997, Dublin, Ireland 2: 761–765.

    Article  Google Scholar 

  13. 13.

    Sun Z, Bebis G, Miller R: Evaluationary Gabor filter optimization with application to vehicle detection. Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM '03), November 2003, Melbourne, Fla, USA 307–314.

    Google Scholar 

  14. 14.

    Viola P, Jones M: Rapid object detection using a boosted cascade of simple features. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 511–518.

    Google Scholar 

  15. 15.

    Shen L, Bai L: AdaBoost Gabor feature selection for classification. Proceeding of Image and Vision Computing Conference (IVCNZ '04), 2004, Akaroa, New Zealand 77–83.

    Google Scholar 

  16. 16.

    Li SZ, Zhang Z: FloatBoost learning and statistical face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(9):1112–1123. 10.1109/TPAMI.2004.68

    Article  Google Scholar 

  17. 17.

    Tourassi GD, Frederick ED, Markey MK, Floyd CE Jr.: Application of the mutual information criterion for feature selection in computer-aided diagnosis. Medical Physics 2001, 28(12):2394–2402. 10.1118/1.1418724

    Article  Google Scholar 

  18. 18.

    Fleuret F: Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research 2004, 5: 1531–1555.

    MathSciNet  MATH  Google Scholar 

  19. 19.

    Weldon TP, Higgins WE, Dunn DF: Efficient Gabor filter design for texture segmentation. Pattern Recognition 1996, 29(12):2005–2015. 10.1016/S0031-3203(96)00047-7

    Article  Google Scholar 

  20. 20.

    Kruger V, Sommer G: Gabor wavelet networks for efficient head pose estimation. Image and Vision Computing 2002, 20(9–10):665–672. 10.1016/S0262-8856(02)00056-2

    Article  Google Scholar 

  21. 21.

    Phillips PJ: Support vector machines applied to face recognition. Proceedings of 1998 Conference on Advances in Neural Information Processing Systems II, November 1999 803–809.

    Google Scholar 

  22. 22.

    Scholkopf B, Mika S, Burges CJC, et al.: Input space versus feature space in Kernel-based methods. IEEE Transactions on Neural Networks 1999, 10(5):1000–1017. 10.1109/72.788641

    Article  Google Scholar 

  23. 23.

    Yang M-H: Kernel eigenfaces vs. Kernel fisherfaces: face recognition using Kernel methods. Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '02), May 2002, Washington, DC, USA 215–220.

    Google Scholar 

  24. 24.

    Baudat G, Anouar F: Generalized discriminant analysis using a Kernel approach. Neural Computation 2000, 12(10):2385–2404. 10.1162/089976600300014980

    Article  Google Scholar 

  25. 25.

    Belhumeur PN, Hespanha JP, Kriegman DJ: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7):711–720. 10.1109/34.598228

    Article  Google Scholar 

  26. 26.

    Kendall M, Stuart A, Ord JK: Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory. Edward Arnold, Paris, France; 1994.

    Google Scholar 

  27. 27.

    Beveridge R, Draper B: Evaluation of Face Recognition Algorithms. 2003.

    Google Scholar 

  28. 28.

    Kepenekci B, Tek FB, Akar GB: Occluded face recognition based on Gabor wavelets. Proceedings of the IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 1: 293–296.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Linlin Shen.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Shen, L., Bai, L. Information Theory for Gabor Feature Selection for Face Recognition. EURASIP J. Adv. Signal Process. 2006, 030274 (2006). https://doi.org/10.1155/ASP/2006/30274

Download citation

Keywords

  • Feature Selection
  • Mutual Information
  • Face Recognition
  • Face Image
  • Kernel Method