Combining different biometric traits with one-class classification

It has been demonstrated in the literature that the combining of different biometric traits is a powerful tool to overcome the limitations imposed by a single biometric system. The fusion of different systems can be approached in different ways. In this work, we consider the pattern classification approach, where the scores of the various systems are used as features to feed the classifiers. More specifically, we are interested in one-class classifiers, and we show that one-class classification could be considered as an alternative to biometric fusion, especially when the data are highly unbalanced or when data from only a single class are available. The results reported for one-class classification on two different databases compares with the standard two-class SVM and surpasses all the conventional classifier combination rules tested.