Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error analysis of pattern recognition systems: the subsets bootstrap
Computer Vision and Image Understanding
Biometric Systems: Technology, Design and Performance Evaluation
Biometric Systems: Technology, Design and Performance Evaluation
ROC confidence bands: an empirical evaluation
ICML '05 Proceedings of the 22nd international conference on Machine learning
Calculation of a composite DET curve
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
IEEE Transactions on Information Forensics and Security
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Biometric authentication performance is often depicted by a detection error trade-off (DET) curve. We show that this curve is dependent on the choice of samples available, the demographic composition and the number of users specific to a database. We propose a two-step bootstrap procedure to take into account the three mentioned sources of variability. This is an extension to the Bolle et al.'s bootstrap subset technique. Preliminary experiments on the NIST2005 and XM2VTS benchmark databases are encouraging, e.g., the average result across all 24 systems evaluated on NIST2005 indicates that one can predict, with more than 75 percent of DET coverage, an unseen DET curve with eight times more users. Furthermore, our finding suggests that with more data available, the confidence intervals become smaller and, hence, more useful.