Evaluation of independence between multiple fingerprints for multibiometrics

Abstract

Multibiometrics provides high recognition accuracy and population coverage by combining different biometric sources. However, some multibiometrics may obtain smaller-than-expected improvement of recognition accuracy if the combined biometric sources are dependent in terms of a false acceptance by mistakenly perceiving biometric features from two different persons as being from the same person. In this paper, we evaluated whether or not features of multiple fingerprints from a same person are statistically independent. By evaluating false acceptance error using matching scores obtained by Verifinger SDK, we confirmed that these features were dependent and the FAR obtained by a fusion of the multiple fingerprints could be affected by the dependence.