Weighted Multi-Class Support Vector Machine for robust face recognition

This paper presents a novel scheme for face recognition using Weighted Multi-class Support Vector Machine (WMSVM). Support Vector Machine (SVM) is well-known powerful tool for solving classification problem. Weighted Support Vector Machines (Weighted SVM) are extension of the SVM. It has been seen that different input vectors make different contribution to the learning of a decision surface. Therefore, different weights are assigned to different data points, so that the Weighted SVM training algorithm learns the decision surface according to the relative importance of data points in the training data. In our proposed WMSVM, probabilistic method is used for weight generation. The generalized two-dimensional Fisher's linear discriminant (G-2DFLD)-based facial features are applied on the proposed WMSVM for recognition. The experimental results on UMIST and AR face database show that the proposed Weighted Multi-class SVM yields higher recognition rate than standard Multi-class SVM.