Bayesian adaptation for user-dependent multimodal biometric authentication
A novel score-level fusion strategy based on Bayesian adaptation for user-dependent multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted for each user based on prior information extracted from a pool of users. Experimental results are reported using on-line signature and fingerprint verification subsystems on the MCYT real bimodal database. The proposed scheme outperforms both user-independent and user-dependent standard approaches. As compared to non-adapted user-dependent fusion, relative improvements of 80% 80 % and 55% 55 % are obtained for small and large training set sizes, respectively.
adaptation multimodal biometrics authentication signature fingerprint verification bayes