Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Digital Image Processing
Practical Biometrics: From Aspiration to Implementation
Practical Biometrics: From Aspiration to Implementation
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Journal of Cognitive Neuroscience
IEEE Transactions on Circuits and Systems for Video Technology
An intelligent multimodal biometric system for high security access
International Journal of Biometrics
An effective colour feature extraction method using evolutionary computation for face recognition
International Journal of Biometrics
Spectral Regression dimension reduction for multiple features facial image retrieval
International Journal of Biometrics
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Multimodal biometrics is an emerging area of research that aims at increasing the reliability of biometric systems through utilising more than one biometric in decision-making process. An effective fusion scheme plays a key role in combining the information presented by the multiple domain experts. Such information can be integrated at several distinct levels, such as sensor level, feature level, match score level, rank level and decision level. This paper describes the combination process of different monomodal expert through rank and decision fusion methods using iris, ear and face biometrics for secure human authentication. For rank-level fusion, plurality voting, Borda count and logistic regression approaches are employed and compared, and for decision-level fusion, AND/OR, majority voting, weighted majority voting and behavioural knowledge space approaches have been implemented and tested. The key contribution of the paper is in comparison of the recognition performance of the developed multimodal system for all of the above approaches. The results indicate that fusing individual modalities improve the overall performance of the biometric system and the logistic regression rank-level fusion results in the highest recognition performance even in the presence of low-quality data.