A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Multimodal Biometric Authentication Using Quality Signals in Mobile Communications
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Improving fusion with margin-derived confidence in biometric authentication tasks
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Qualitative fusion of normalised scores in multimodal biometrics
Pattern Recognition Letters
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Feature selection for support vector machine-based face-iris multimodal biometric system
Expert Systems with Applications: An International Journal
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This paper presents an investigation into the effects, on the accuracy of multimodal biometrics, of introducing unconstrained cohort normalisation (UCN) into the score-level fusion process. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This study aims to explore the potential usefulness of the said score normalisation technique in face biometrics and to investigate its effectiveness for enhancing the accuracy of multimodal biometrics. The experimental investigations involve the two recognition modes of verification and open-set identification, in clean mixed-quality and degraded data conditions. Based on the experimental results, it is demonstrated that the capabilities provided by UCN can significantly improve the accuracy of fused biometrics. The paper presents the motivation for, and the potential advantages of, the proposed approach and details the experimental study.