Learning Radial Basis Function Model with Matching Score Quality for Person Authentication in Multimodal Biometrics

Recently multimodal biometrics technology that employs more than two types of biometrics data has been popularly used for person authentication and verification. In particular, the score-level fusion approach which combines matching scores from unimodal systems to make final decision has gained lots of attentions. In most of these works, however, they assume all the matching scores to be of the same quality. This assumption may cause the problem not to reflect such situation that the qualities of the matching scores from certain unimodal systems are relatively low. To deal with this problem, we propose the RBF based score-level fusion approach which incorporates the quality information of the scores in developing classification models. According to our experimental results, the proposed method using quality information showed its superiority in the performance of person authentication to the usual RBF based score-level fusion without using quality information.