Comparison of Face Verification Results on the XM2VTS Database
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Validating a Biometric Authentication System: Sample Size Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting global and local decisions for multimodal biometrics verification
IEEE Transactions on Signal Processing - Part II
A Biometric Menagerie Index for Characterising Template/Model-Specific Variation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
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
Improving biometric verification systems by fusing Z-norm and F-norm
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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It has been shown that the authentication performance of a biometric system is dependent on the models/templates specific to a user. As a result, some users may be more easily recognised or impersonated than others. We propose a model-specific (or user-specific) likelihood based score normalisation procedure that can reduce this dependency. While in its original form, such an approach is not feasible due to the paucity of data, especially of the genuine users, we stabilise the estimates of local model parameters with help of the user-independent (hence global) parameters. The proposed approach is shown to perform better than the existing known score normalisation procedures, e.g., the Z-, F- and EER-norms, in the majority of experiments carried out on the XM2VTS database. While these existing procedures are linear functions, the proposed likelihood based approach is quadratic but its complexity is further limited by a set of constraints balancing the contributions of the local and the global parameters, which are crucial to guarantee good generalisation performance.