Abstract
Dense fingerprint registration in the preprocessing stage plays a vital role in the subsequent fingerprint fusion, mosaic, and recognition. However, the existing conventional methods are limited by handcraft features, while the methods based on deep learning lack a large amount of ground truth displacement fields. To overcome these limitations, we propose a self-supervised learning model to directly output densely registered fingerprints. With a spatial transformation network (STN) connected after fully convolutional network (FCN), image deformation interpolation can be achieved to obtain the registered image. Self-supervised training is achieved by maximizing the similarity of images, without the need for ground truth displacement fields. We evaluate the proposed model on publicly available datasets of internal-external fingerprint image pairs. The results demonstrate that the accuracy of the model is comparable to that of the conventional fingerprint registration while executing orders of magnitude faster.
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Acknowledgments
This work was supported in part by Natural Science Foundation of Zhejiang Province under Grant LY19F050011, National Natural Science Foundation of China under Grant 61976189, 61905218, 62076220, and Fundamental Research Funds for the Provincial Universities of Zhejiang under grant RF-C2019001.
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Yu, Y., Wang, H., Zhang, Y., Chen, P. (2021). A STN-Based Self-supervised Network for Dense Fingerprint Registration. 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_31
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DOI: https://doi.org/10.1007/978-3-030-86608-2_31
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