Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
A Biometric Menagerie Index for Characterising Template/Model-Specific Variation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Meta-Recognition: The Theory and Practice of Recognition Score Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating Model-Specific Score Distribution in Speaker Verification Systems
IEEE Transactions on Audio, Speech, and Language Processing
Customizing biometric authentication systems via discriminative score calibration
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Biometric zoos: Theory and experimental evidence
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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User-specific score normalization which is related to biometric menagerie has received a lot of attention in the last decade. It is a one-to-one mapping function such that after its application, only a global threshold is needed. In this paper we propose a novel user-specific score normalization framework based on the fusion of Z-norm and F-norm. In this framework, firstly one post-processes the biometric system scores with Z-norm and F-norm procedures. Then, one feeds the resulting two dimensional normalized score vector to a fusion classifier to obtain a final normalized score. Using logistic regression as a fusion classifier, experiments carried out on 13 face and speech systems of the XM2VTS database show that the proposed strategy is likely to improve over the original separate score normalization schemes (F-norm or Z-norm). Furthermore, this novel strategy turns out to be the best strategy for applications requiring low false acceptance rate.