Abstract—Face recognition has been an active research area over the last 30 years. It has been studied by scientists from different areas of psychophysical sciences and those from different areas of computer sciences. Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVMs on the ORL face database, Georgiatech face database, and personally prepared database, containing quite a high degree of variability in expression, pose, and facial details. We compare the performance of RBF kernel based SVM with SVMs based on other kernel functions for face recognition. Comparison is also performed on different available techniques of developing multiclass SVMs.
Index Terms—Face recognition, support vector machine, radial basis function.
Ashish Chittora is with the Amity School of Engineering and technology,
Sector-125, Noida, Uttar Pradesh (e-mail: email@example.com,
Tel.: + 91-9654329857).
Om Mishra is with the G B Pant Government Engineering College, Okhla, New Delhi.
Cite: Ashish Chittora and Om Mishra, "Face Recognition Using RBF Kernel Based Support Vector Machine," International Journal of Future Computer and Communication vol. 1, no. 3, pp. 280-283, 2012.