Score Selection Techniques for Fingerprint Multi-Modal Biometric Authentication

Fingerprints are one of the most used biometrics for automatic personal authentication. Unfortunately, it is often difficult to design fingerprint matchers exhibiting the performances required in real applications. To meet the application requirements, fusion techniques based on multiple matching algorithms, multiple fingerprints, and multiple impressions of the same fingerprint, have been investigated. However, no previous work has investigated selection strategies for biometrics. In this paper, a score selection strategy for fingerprint multi-modal authentication is proposed. For each authentication task, only one score is dynamically selected so that the genuine and the impostor users’ scores distributions are mainly separated. Score selection is performed by first estimating the likelihood that the input pattern is an impostor or a genuine user. Then, the min score is selected in case of an impostor, while the max score is selected in case of a genuine user. Reported results show that the proposed selection strategy can provide better performances than those of commonly used fusion rules.