Face recognition using support vector model classifier for user authentication

Abstract

Most existing user authentication approaches for detecting fraud in e-commerce applications have focused on Secure Sockets Layer (SSL)-based authentication to inspect a username and a password from a server, rather than the inspection of personal biometric information. Because of the lack of support for mutual authentication or two-way authentication between a consumer and a mercantile agent, one-way SSL authentication cannot prevent man-in-the-middle attacks. In practice, in user authentication systems, machine learning and the generalisation capability of support vector models (SVMs) are used to guarantee a small classification error. This study developed an online face-recognition system by training an SVM classifier based on user facial features associated with wavelet transforms and a spatially enhanced local binary pattern. A cross-validation scheme and SVMs associated with the Olivetti Research Laboratory database of user facial features were used for solving classification precision problems. Experimental results showed that the classification error decreased with an increase in the size of the training samples. By using the aggregation of both the low-resolution and the high-resolution face image samples, the global precision of face recognition was over 97% with tenfold cross-validation scheme for an image data size of 168 and 341, respectively. Overall, the proposed scheme provided a higher precision of face recognition compared with the average precision for low-resolution face image (approximately 89%) of the existing schemes.