Abstract
Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method.
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Acknowledgements
The work is partially supported by the National Natural Science Foundation of China under grants no. 62076163 and 91959108, the Shenzhen Fundamental Research fund JCYJ20190808163401646, and the Innovation Team Project of Colleges in Guangdong Province under grants no. 2020KCXTD040.
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Zeng, W., Zhang, W., Liu, F., Tan, X., Li, Q. (2022). A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints. 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_3
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DOI: https://doi.org/10.1007/978-3-031-20233-9_3
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