Shift Invariant Support Vector Machines Face Recognition System

In this paper, we present a new method for incorporating global shift invariance in support vector machines. Unlike other approaches which incorporate a feature extraction stage, we first scale the image and then classify it by using the modified support vector machines classifier. Shift invariance is achieved by replacing dot products between patterns used by the SVM classifier with the maximum cross-correlation value between them. Unlike the normal approach, in which the patterns are treated as vectors, in our approach the patterns are treated as matrices (or images). Crosscorrelation is computed by using computationally efficient techniques such as the fast Fourier transform. The method has been tested on the ORL face database. The tests indicate that this method can improve the recognition rate of an SVM classifier.

Authors:

• J. Ruiz-Pinales
• J. J. Acosta-Reyes
• A. Salazar-Garibay
• R. Jaime-Rivas

Keywords:

• Face recognition
• support vector machines
• shiftinvariance
• image registration.

References:

[1] K.M. Lam and H. Yan, "An Analytic-to-holistic Approach for Face
Recognition Based on a Single Frontal View", IEEE Trans. on Pattern
Analysis and Machine Intelligence, vol. 20, no. 7, pp. 673-686, 1998.
[2] M. Turk, A. Pentland, "Eigenfaces for Recognition", J. Cognitive
Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[3] M.S. Kamel, H.C. Shen, A.K.C. Wong, T.M. Hong and R.I. Campeanu,
"Face Recognition using Perspective Invariant Features", Pattern.
Recognition Letters, vol. 15, no. 9, pp. 877-883, 1994.
[4] W. Zhao, R. Chellappa, P.J. Phillips and A. Rosenfeld, "Face
Recognition: A Literature Survey", ACM Computing Surveys, vol. 35,
no. 4, pp. 399-458, 2003.
[5] V.N. Vapnik, The Nature of Statistical Learning Theory. 2nd edn.
Springer-Verlag, New York, 2000.
[6] G. Guo, S. Z. Li and C. Kapluk, "Face recognition by support vector
machines", Image and Vision Computing, vol. 19, pp. 631-638, 2001.
[7] D. DeCoste and B. Schölkopf, "Training Invariant Support Vector
Machines", Machine Learning, vol. 46, pp. 161-190, 2002.
[8] C. Cortes and V. Vapnik, "Support-Vector Networks'', Machine
Learning, vol. 20, no. 3, pp. 273-297, 1995.
[9] C.-W. Hsu and C.-J. Lin, "A comparison of methods for multi-class
support vector machines", IEEE Transactions on Neural Networks, vol.
13, no. 2, pp. 415-425, 2002.
[10] B. Schölkopf, C. Burges and V. Vapnik, "Incorporating Invariances in
Support Vector Learning Machines", In Lecture Notes in Computer
Science, vol. 1112, pp. 47-52, 1996.
[11] L. G. Brown, "A survey of image registration techniques," ACM
Computing Surveys, vol. 24, no. 4, pp. 325-376, 1992.
[12] R. Fernandez and E. Viennet, "Face identification using support vector
machines", in Proceedings of the European Symposium on Artificial
Neural Networks (ESANN99), 1999, pp.195-200.
[13] C. C. Chang and C. J. Lin, "LIBSVM: a library for support vector
machines", 2001. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[14] M. Frigo and S. G. Johnson, "The Design and Implementation of
FFTW3," Proceedings of the IEEE, vol. 93, no. 2, pp. 216-231, 2005.
[15] T. Jaakkola, M. Diekhans and D. Haussler, "Using the Fisher Kernel
Method to Detect Remote Protein Homologies", In Proceedings of the
Seventh international Conference on intelligent Systems For Molecular
Biology, 1999, pp. 149-158.