Fusion of fingerprint and heartbeat biometrics using fuzzy adaptive genetic algorithm

The fusion of fingerprint and heartbeat is a promising multimodal biometric method especially for remote authentication using mobile computing devices. We propose the use of fuzzy adaptive genetic algorithm for the improvement of authentication performance of this multimodal biometrics. This method computes the optimal weights required for the fusion of matching scores from two modalities. This method is tested by a large database of multimodal biometrics composed of fingerprint and heartbeat signal captured from fingers. The proposed method outperforms existing methods in terms of authentication performances such as equal error rate (EER) and area under the ROC curve (AUR).