Feature level fusion of fingerprint and finger vein biometrics
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
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Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this paper, we present a multimodal biometric system that is based on the fusion of face and fingerprint biometrics. For face recognition, we employ uniform local binary patterns (ULBP), while minutiae extraction is used for fingerprint recognition. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.