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Face Recognition using Features Combination and a New Non-linear Kernel

Authors: Essam Al Daoud


To improve the classification rate of the face recognition, features combination and a novel non-linear kernel are proposed. The feature vector concatenates three different radius of local binary patterns and Gabor wavelet features. Gabor features are the mean, standard deviation and the skew of each scaling and orientation parameter. The aim of the new kernel is to incorporate the power of the kernel methods with the optimal balance between the features. To verify the effectiveness of the proposed method, numerous methods are tested by using four datasets, which are consisting of various emotions, orientations, configuration, expressions and lighting conditions. Empirical results show the superiority of the proposed technique when compared to other methods.

Keywords: Face recognition, Gabor wavelet, LBP, Non-linearkerner

Digital Object Identifier (DOI):

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[1] C. Tenllado, J.I. Gomez, J. Setoain, et al., "Improving face recognition by combination of natural and Gabor faces," Pattern Recognition Letters, 31 (11): 1453- 1460, 2010.
[2] J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
[3] Li J,Yu.D.W ,Kuang G, "The Research on Face Recognition Approaches of Infrared Imagery
[J]", Journal of National University of Defense Technology, 28 (2), pp.73-76, 2006.
[4] N. Liu, J. Lai, and H. Qiu, "Robust Face Recognition by Sparse Local Features from a Single Image under Occlusion," 2011 Sixth International Conference on Image and Graphics, pp. 500-505, 2011.
[5] U. Prabhu, and M. Savvides, "Unconstrained Pose Invariant Face Recognition Using 3D Generic Elastic Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 10, pp. 1952-1961, Oct. 2011.
[6] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, Y. Ma, :Towards a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 99, DOI10.1109/TPAMI.2011.112 ,2011.
[7] Lior Wolf, Tal Hassner, Member, IEEE, and Yaniv Taigman "Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 10, pp. 1978-1990, Oct. 2011.
[8] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face Recognition by Independent Component Analysis," IEEE Transaction on Neural Networks, vol. 13, no. 6, pp. 1450-1462, 2002.
[9] E. Meyers and L. Wolf, "Using Biologically Inspired Features for Face Processing," Int-l J. Computer Vision, vol. 76, no. 1, pp. 93-104, 2008.
[10] T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: application to face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037- 2041, 2006.
[11] Prasetiyo, M. Khalid, R. Yusof, and F. Meriaudeau, "A Comparative Study of Feature Extraction Methods for Wood Texture Classification, " 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems, pp23-29, 2010.
[12] H. Zhang, A. Berg, M. Maire, and J. Malik, "SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
[13] (last Accessed August 2011).
[14] Faces95 from Essex university (last Accessed August 2011).
[15] Indian Face Database http://viswww. (last Accessed August 2011).
[16] AT&T facedatabase.html (last Accessed August 2011).Yale face database