Membership authentication in the dynamic group by face classification using SVM ensemble

Abstract

This paper presents a method for authenticating an individual's membership in a dynamic group without revealing the individual's identity and without restricting the group size and/or the members of the group. We treat the membership authentication as a two-class face classification problem to distinguish a small size set (membership) from its complementary set (non-membership) in the universal set. In the authentication, the false-positive error is the most critical. Fortunately, the error can be validly removed by using the support vector machine (SVM) ensemble, where each SVM acts as an independent membership/non-membership classifier and several SVMs are combined in a plurality voting scheme that chooses the classification made by more than the half of SVMs. For a good encoding of face images, the Gabor filtering, principal component analysis and linear discriminant analysis have been applied consecutively to the input face image for achieving effective representation, efficient reduction of data dimension and strong separation of different faces, respectively. Next, the SVM ensemble is applied to authenticate an input face image whether it is included in the membership group or not. Our experiment results show that the SVM ensemble has the ability to recognize non-membership and a stable robustness to cope with the variations of either different group sizes or different group members. Also, we still get a reasonable membership recognition rate in spite of the limited number of membership training data. (C) 2002 Elsevier Science B.V. All rights reserved.