SV-M/D: Support vector machine-singular value decomposition based face recognition

Abstract

This paper presents a novel method for Face Recognition (FR) by applying Support Vector Machine (SVM) in addressing this Computer Vision (CV) problem. The SVM is a capable learning classifier capable of training polynomials, neural networks and RBFs. Singular Value Decomposition (SVD) are used for feature extraction and while SVM for classification. The proposed algorithm is tested on four databases, viz., FERET, FRGC Ver. 2.0, CMU-PIE and Indian Face Database. The singular values are filtered, sampled and classified using Gaussian RBF kernal for SVM. The results are compared with other known methods to establish the advantage of SV-M/D. The recall rate for the proposed system is about 90%. The colossal augmentation is due to the simple but efficient feature extraction and the ability to learn in a high dimensional space.