Adapted user-dependent multimodal biometric authentication exploiting general information
Pattern Recognition Letters
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Pattern Recognition Letters
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Expert Systems with Applications: An International Journal
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AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, we address the multimodal biometric decision fusion problem. By exploring into the user-specific approach for learning and threshold setting, four possible paradigms for learning and decision making are investigated. Since each user requires a decision hyperplane specific to him in order to achieve good verification accuracy, those tedious iterative training methods like the neural network approach would not be suitable. We propose to use a model that requires only a single training step for this application. The four global and local learning and decision paradigms are then explored to observe their decision capability. Besides the proposal of a relevant receiver operating characteristic performance for the local decision, extensive experiments were conducted to observe the verification performance for fusion of two and three biometrics.