Abstract
Fast and accurate fingerprint classification is very important in large-scale fingerprint identification system. At present, fingerprint classification model has many problems such as complicated operation, lots of parameters, massive data. In this paper, we present a lightweight neural network for automatic extraction features and classification of fingerprint images. Fingerprint Region of Interest (ROI) images is regarded as the input of the network and fused with the shallow feature map to obtain accurate trend information of the shallow middle line. Transfer learning and fingerprint directional field map are combined to pre-train the lightweight network, then the parameters of the network are optimized and experimentally verified. Experimental results show that the fingerprint ROI is integrated into the deep features, which can improve the fingerprint classification effect. The transfer of the lightweight network model can reduce the network requirements for the target domain data and improve the classification performance of small sample fingerprint images.
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References
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Hu, J., Lu, J., Zhou, X., et al.: Discriminative transfer learning for single-sample face recognition, pp. 272–277. In: International Conference on Biometrics (ICB). IEEE (2015)
Geng, M., Wang, Y., Xiang, T., et al.: Deep transfer learning for person re-identification. arXiv preprint arXiv:1611.05244 (2016)
Tan, B., Zhang, Y., Pan, S.J., et al.: Distant domain transfer learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Xu, L., Xiang, J., Yuan, X.: Transferring rich deep features for facial beauty prediction [EB/OL], 20 March 2018. https://arxiv.org/pdf/1803.07253.pdf
Iandola, F.N., et al.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0. 5Â MB model siz. arXiv preprint arXiv:1602.07360 (2016)
Akram, M.U., Nasir, S., Tariq, A., et al.: Improved fingerprint image segmentation using new modified gradient based technique. In: Canadian Conference on Electrical and Computer Engineering, CCECE. IEEE (2008). 001967-001972
Wang, J.: Transfer Learning Tutorial. Technical report 2018. 4. http://jd92.wang
Acknowledgement
This Work is Supported by National Natural Science Foundation of China (No. 61771347), Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province (No. 2018KZDXM073).
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Gan, J., Qi, L., Bai, Z., Xiang, L. (2019). Fingerprint Classification Based on Lightweight Neural Networks. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_4
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DOI: https://doi.org/10.1007/978-3-030-31456-9_4
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