Face verification based on bagging RBF networks

Authors:
Yunhong Wang;Yiding Wang;Anil K. Jain;Tieniu Tan
Affiliations:
School of Computer Science and Engineering, Beihang University, Beijing, China;Graduate School, Chinese Academy of Sciences, Beijing, China;Department of Computer Science & Engineering, Michigan State University, East Lansing, MI;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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Abstract

Face verification is useful in a variety of applications. A face verification system is vulnerable not only to variations in ambient lighting, facial expression and facial pose, but also to the effect of small sample size during the training phase. In this paper, we propose an approach to face verification based on Radial Basis Function (RBF) networks and bagging. The technique seeks to offset the effect of using a small sample size during the training phase. The RBF networks are trained using all available positive samples of a subject and a few randomly selected negative samples. Bagging is then applied to the outputs of these RBF-based classifiers. Theoretical analysis and experimental results show the validity of the proposed approach.