Abstract
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.
Original language | English |
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Pages (from-to) | 3059-3072 |
Number of pages | 14 |
Journal | IEEE Transactions on Signal Processing |
Volume | 52 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2004 Oct |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering