Abstract
For personal identification, three modalities of fingers, fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP), can be used respectively. Fusing these modalities together as a whole biometric measure should naturally highlight the finger superiority in convenience and universality as well as recognition accuracy improvement. In this paper, a new finger recognition method based on granular computing is proposed. This method can synergistically combine the features of FP, FV and FKP in feature level and provide robustness to finger pose variation. The proposed granular space is constructed in bottom-up manner with three granule-layers. And a coarse-to-fine scheme is used for granule matching. Experiments are performed on a self-built database with three modalities to validate the proposed method in personal identification.
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Peng, J., Li, Y., Li, R., Jia, G., Yang, J. (2014). Multimodal Finger Feature Fusion and Recognition Based on Delaunay Triangular Granulation. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_32
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DOI: https://doi.org/10.1007/978-3-662-45643-9_32
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