On the Error-Reject Trade-Off in Biometric Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Score normalization in multimodal biometric systems
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive client-impostor centric score normalization: a case study in fingerprint verification
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Automatic detection of auditory salience with optimized linear filters derived from human annotation
Pattern Recognition Letters
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The verification performance of biometric systems is normally evaluated using the receiver operating characteristic (ROC) or detection error trade-off (DET) curve. We propose two new ideas for statistical evaluation of biometric systems based on these data. The first is a new way to normalize match score distributions. A normalized match score, $\hat{t}$, is calculated as a function of the angle from a representation of (FMR, FNMR) values in polar coordinates from some center. This has the advantage that it does not produce counterintuitive results for systems with unusual DET performance. Secondly, building on this normalization we develop a methodology to calculate an average DET curve. Each biometric system is represented in terms of $\hat{t}$ to allow genuine and impostor distributions to be combined, and an average DET is then calulated from these new distributions. We then show that this method is equivalent to direct averaging of DET data along each angle from the center. This procedure is then applied to data from a study of human matchers of facial images.