Predicting fingerprint biometrics performance from a small gallery

Predicting the performance of a biometrics is an important problem in a real-world application. In this paper, we present a binomial model to predict both the fingerprint verification and identification performance. The match and non-match scores are computed, using the number of corresponding triangles as the match metric, between the query and gallery fingerprints. The triangles are formed using the minutiae features. The match score and non-match score in a binomial prediction model are used to predict the performance on large (relative to the size of the gallery) populations from a small gallery. We apply the model to the entire NIST-4 database and show the results for both the fingerprint verification and the identification.