Face recognition with support vector machine

Abstract

Face detection and recognition have received much interest over pass ten years due to the many applications range from access control to driver’s licenses. In general, face recognition systems can be classified as: geometric featurebased approaches, template matching and neural approaches. One main drawback of geometric feature-based approaches is not easy to extract and measure the feature. Compared to geometric feature-based approaches, a templatebased approach recognizes faces as a whole. The main idea of these methods is to transform the face image into a low dimensional space. Although the approach of template matching and neural are very efficient, the computation is more complex compared to other algorithms. Support Vector Machines recently have been regarded as an effective statistical learning method for pattern recognition. In this paper, we introduce a support vector machine for face recognition. Next, we have shown the experiment result using the polynomial kernel.