An integrated prediction model for biometrics

Authors:
Rong Wang;Bir Bhanu;Hui Chen
Affiliations:
Center for Research in Intelligent Systems, University of California, Riverside, Riverside, California;Center for Research in Intelligent Systems, University of California, Riverside, Riverside, California;Center for Research in Intelligent Systems, University of California, Riverside, Riverside, California
Venue:
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Year:
2005

Citing 4
Cited 3

Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Using similarity scores from a small gallery to estimate recognition performance for larger galleries

AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Predicting large population data cumulative match characteristic performance from small population data

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Models of large population recognition performance

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Predicting fingerprint biometrics performance from a small gallery

Pattern Recognition Letters
Credence estimation and error prediction in biometric identity verification

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

Pattern Recognition

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Abstract

This paper addresses the problem of predicting recognition performance on a large population from a small gallery. Unlike the current approaches based on a binomial model that use match and non-match scores, this paper presents a generalized two-dimensional model that integrates a hypergeometric probability distribution model explicitly with a binomial model. The distortion caused by sensor noise, feature uncertainty, feature occlusion and feature clutter in the gallery data is modeled. The prediction model provides performance measures as a function of rank, population size and the number of distorted images. Results are shown on NIST-4 fingerprint database and 3D ear database for various sizes of gallery and the population.