Abstract
In this paper we present a novel Gabor-SVM method for face recognition by integrating the Gabor wavelet representation of face images and SVM classifier. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to deal with the variations due to illumination and facial expression changes. The principal components analysis (PCA) method is then used to reduce the dimensionality of the extracted Gabor features. With the reduced Gabor features, SVM is trained and then employed to do the recognition tasks. The performance of Gabor-SVM method is compared with the standard PCA-NC (Eigenfaces) method and PCA-SVM method on a subset of AR face database. The experiment results demonstrate the efficiency and superiority of the proposed Gabor-SVM method.
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References
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Survey 35, 399–458 (2003)
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 2, 1160–1169 (1985)
Lee, T.S.: Image Representation Using 2D Gabor Wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 959–971 (1996)
Lades, M., Vorbruggen, J.C., Buhmann, J., Lange, J., Malsburg, C.V.D., Wurtz, R.P., Konen, W.: Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers 42, 300–310 (1993)
Wiskott, L., Fellous, J.M., Kruger, N., Malsburg, C.V.D.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)
Liu, C.J.: Gabor-based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 572–581 (2004)
Donato, G., Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J.: Classifying Facial Actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 974–989 (1999)
Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, New York (1998)
Phillips, P.J.: Support Vector Machines Applied to Face Recognition. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 803–809. MIT Press, Cambridge (1999)
Guo, G.D., Li, S.Z., Chan, K.L.: Support Vector Machines for Face Recognition. Image and Vision Computing 19, 631–638 (2001)
Deniz, O., Castrillon, M., Hernandez, M.: Face Recognition Using Independent Component Analysis and Support Vector Machines. Pattern Recognition Letters 24, 2153–2157 (2003)
Martinez, A.M., Benavente, R.: The AR Face Database. Technical Report 24, CVC (1998)
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© 2005 Springer-Verlag Berlin Heidelberg
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Liang, Y., Gong, W., Pan, Y., Li, W., Hu, Z. (2005). Gabor Features-Based Classification Using SVM for Face Recognition. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_20
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DOI: https://doi.org/10.1007/11427445_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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