Personalized learning and decision for multimodal biometrics

In this paper, we address the multi-modal 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 which 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 capabilities. Besides proposal of a relevant receiver operating characteristic performance for local decision, extensive experiments were conducted to observe the verification performance for fusion of three biometrics.