Abstract
Finger carrying finger-vein (FV), fingerprint (FP), and finger-knuckle-print (FKP) simultaneously has become a research focus in the field of multimodal biometric. In this paper, a finger trimodal features coding fusion method based on vector of locally aggregated descriptors (VLAD) is proposed. First, three feature extraction models based on convolutional neural network (CNN) are designed to extract finger trimodal features individually. Then, under the direct of VLAD, feature maps of finger trimodal from feature extraction models based on CNN are encoded respectively. Finally, finger trimodal coded features are fused in series to obtain fusion features. The recognition accuracy of fusion features obtained by the proposed method can reach 99.76%, and experimental results demonstrate that the fusion features possess excellent individual characteristic expression ability, which is favourable to improve recognition accuracy and robustness.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 62076166 and 61806208.
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Wen, M., Ye, Z., Yang, J. (2022). Finger Trimodal Features Coding Fusion Method. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_47
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DOI: https://doi.org/10.1007/978-3-031-20233-9_47
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