Predicting fingerprint biometrics performance from a small gallery

Authors:
Rong Wang;Bir Bhanu
Affiliations:
Center for Research in Intelligent Systems, University of California, Riverside, CA 92521, USA;Center for Research in Intelligent Systems, University of California, Riverside, CA 92521, USA
Venue:
Pattern Recognition Letters
Year:
2007

Citing 9
Cited 3

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Fingerprint Indexing Based on Novel Features of Minutiae Triplets

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AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication

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A new efficient method of fingerprint image enhancement

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Learning small gallery size for prediction of recognition performance on large populations

Pattern Recognition

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Abstract

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.