Over-complete feature generation and feature selection for biometry

In this paper a novel method for obtaining an appropriate representation of patterns is presented. The information is extracted using an over-complete global feature combination, and then the most useful features are selected by Sequential Forward Floating Selection (SFFS). This new method has been tested in two problems: trained integration of iris and face biometrics; on-line signature verification system based on global information and a one-class classifier (Parzen Window Classifier). To the best of our knowledge, this is the first work that studies and proposes a set of “artificial” features for combining biometric matchers, created starting from the scores of the matchers. We show that a classifier trained on such set of features gains a noticeable performance improvement with respect to fixed fusion rules and other trained fusion methods. Moreover, we show that an on-line signature matcher based on the “artificial” features gains a noticeable performance improvement with respect to a matcher based on the “original” global features.