Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Size Test Set Gives Good Error Rate Estimates?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting Performance of Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bounds on Shape Recognition Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Indexing Based on Novel Features of Minutiae Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face Recognition Vendor Test 2002
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Error analysis of pattern recognition systems: the subsets bootstrap
Computer Vision and Image Understanding
On the Small Sample Performance of Boosted Classifiers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Models for Predicting Recognition Performance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Validating a Biometric Authentication System: Sample Size Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint matching by genetic algorithms
Pattern Recognition
Predicting fingerprint biometrics performance from a small gallery
Pattern Recognition Letters
Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance prediction for individual recognition by gait
Pattern Recognition Letters
Models of large population recognition performance
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An integrated prediction model for biometrics
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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This paper addresses the estimation of a small gallery size that can generate the optimal error estimate and its confidence on a large population (relative to the size of the gallery) which is one of the fundamental problems encountered in performance prediction for object recognition. It uses a generalized two-dimensional prediction model that combines a hypergeometric probability distribution model with a binomial model and also considers the data distortion problem in large populations. Learning is incorporated in the prediction process in order to find the optimal small gallery size and to improve the prediction. The Chernoff and Chebychev inequalities are used as a guide to obtain the small gallery size. During the prediction, the expectation-maximization (EM) algorithm is used to learn the match score and the non-match score distributions that are represented as a mixture of Gaussians. The optimal size of the small gallery is learned by comparing it with the sizes obtained by the statistical approaches and at the same time the upper and lower bounds for the prediction on large populations are obtained. Results for the prediction are presented for the NIST-4 fingerprint database.