Using Genetic Algorithms to Find Person-Specific Gabor Feature Detectors for Face Indexing and Recognition

  • Sreekar Krishna
  • John Black
  • Sethuraman Panchanathan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

In this paper, we propose a novel methodology for face recognition, using person-specific Gabor wavelet representations of the human face. For each person in a face database a genetic algorithm selects a set of Gabor features (each feature consisting of a particular Gabor wavelet and a corresponding (x, y) face location) that extract facial features that are unique to that person. This set of Gabor features can then be applied to any normalized face image, to determine the presence or absence of those characteristic facial features. Because a unique set of Gabor features is used for each person in the database, this method effectively employs multiple feature spaces to recognize faces, unlike other face recognition algorithms in which all of the face images are mapped into a single feature space. Face recognition is then accomplished by a sequence of face verification steps, in which the query face image is mapped into the feature space of each person in the database, and compared to the cluster of points in that space that represents that person. The space in which the query face image most closely matches the cluster is used to identify the query face image. To evaluate the performance of this method, it is compared to the most widely used subspace method for face recognition: Principle Component Analysis (PCA). For the set of 30 people used in this experiment, the face recognition rate of the proposed method is shown to be substantially higher than PCA.

Keywords

Face Recognition Recognition Rate Face Image Principle Component Analysis Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press (1975)Google Scholar
  2. 2.
    Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  3. 3.
    Etemad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. Journal of Optical Society of America, 1724–1733 (1997)Google Scholar
  4. 4.
    Lee, T.S.: Image representation using 2D Gabor wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 959–971 (1996)CrossRefGoogle Scholar
  5. 5.
    Shen, L., Bai, L.: Gabor wavelets and kernel direct discriminant analysis for face recognition. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, August 2004, vol. 1(23-26), pp. 284–287 (2004)Google Scholar
  6. 6.
    Liu, C., Wechsler, H.: Independent component analysis of Gabor features for face recognition. IEEE Transactions on Neural Networks 14(4), 919–928 (2003)CrossRefGoogle Scholar
  7. 7.
    Liu, C., 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
  8. 8.
    Duc, B., Fischer, S., Bigun, J.: Face authentication with sparse grid Gabor information. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1997, April 1997, vol. 4(21-24), pp. 3053–3056 (1997)Google Scholar
  9. 9.
    Kalocsai, P., Neven, H., Steffens, J.: Statistical analysis of Gabor-filter representation. In: Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16, pp. 360–365 (1998)Google Scholar
  10. 10.
    Liu, Y., Chongqing: Face recognition using kernel principal component analysis and genetic algorithms. In: Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, September 2002, pp. 337–343 (2002)Google Scholar
  11. 11.
    Xu, Y., Li, B., Wang, B.: Face recognition by fast independent component analysis and genetic algorithm. In: The Fourth International Conference on Computer and Information Technology, CIT 2004, September 14-16, pp. 194–198 (2004)Google Scholar
  12. 12.
    Black, J., Gargesha, M., Kahol, K., Kuchi, P., Panchanathan, S.: A Framework for Performance Evaluation of Face Recognition Algorithms. In: ITCOM, Internet Multimedia Systems II, Boston (July 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sreekar Krishna
    • 1
  • John Black
    • 1
  • Sethuraman Panchanathan
    • 1
  1. 1.Center for Cognitive Ubiquitous Computing (CUbiC)Arizona State UniversityTempe

Personalised recommendations