Support vector machine with local summation kernel for robust face recognition

This paper presents support vector machine (SVM) with local summation kernel for robust face recognition. In recent years, the effectiveness of SVM and local features is reported. However, conventional methods apply one kernel to global features. The effectiveness of local features is not used in those methods. In order to use the effectiveness of local features in SVM, one kernel is applied to local features. It is necessary to compute one kernel value from local kernels in order to use the local kernels in SVM. In this paper, the summation of local kernels is used because it is robust to occlusion. The robustness of the proposed method under partial occlusion is shown by the experiments using the occluded face images. In addition, the proposed method is compared with the global kernel based SVM. The recognition rate of the proposed method is over 80% under large occlusion, while the recognition rate of the SVM with global Gaussian kernel decreases dramatically.

[1]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[2]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[3]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

[4]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[5]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[8]  David Haussler,et al.  Convolution kernels on discrete structures , 1999 .

[9]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[11]  Ming-Hsuan Yang,et al.  Face Recognition Using Kernel Methods , 2001, NIPS.

[12]  Tomaso A. Poggio,et al.  Face recognition with support vector machines: global versus component-based approach , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[14]  Kazuhiro Hotta,et al.  A View-Invariant Face Detection Method Based on Local PCA Cells , 2004, J. Adv. Comput. Intell. Intell. Informatics.