Fusion of biometric systems using one-class classification

One of the main requirements of biometric systems is the ability of producing very low false acceptation rate, which very often can be achieved only by combining different biometric traits. The literature has shown that the pattern classification approach usually surpasses the classifier combination approach for this task. In this work we take into account the pattern classification approach, but considering the one-class classification approach. We show that one-class classification could be considered as an alternative for biometric fusion specially when the data is highly unbalanced or data from a single class is available. The results for one-class classification reported in this paper compares to the standard two-class SVM and surpasses all the conventional classifier combination rules tested.