Abstract
Finger which contains abundant biometric information, has played an important role in the field of identification recognition. Due to the complexity of fingerprint (FP), finger-vein (FV) and finger-knuckle (FKP), traditional feature-level fusion methods perform poorly. This paper proposed an end-to-end finger trimodal features fusion and recognition model based on CNN. For the purpose of constructing an end-to-end model, finger three-modal features extraction module and finger trimodal features fusion module are embedded in CNN. The finger three-modal features extraction module is composed of three parallel and independent CNNs, which are used to extract features from finger trimodal images separately. The finger trimodal features fusion module contains two convolution layers, through which fusion feature can be obtained. The experimental results show that the model proposed in this paper can get high recognition accuracy 99.83%. It shows that the fusion feature obtained by the proposed model possessing good individual characterization ability can effectively improve recognition accuracy.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62076166 and 61806208, in part by the Rural Science and Technology Commissioner Project of Guangdong Provincial Science and Technology Department under Grant KPT20200220.
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Wen, M., Zhang, H., Yang, J. (2021). End-To-End Finger Trimodal Features Fusion and Recognition Model Based on CNN. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_5
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DOI: https://doi.org/10.1007/978-3-030-86608-2_5
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