Abstract
Traditional fingerprint recognition methods based on minutiae have shown great success on for high-quality fingerprint images. However, the accuracy rates are significantly reduced for signatured fingermark on the contract. This paper proposes a signatured fingermark recognition method based on deep learning. Firstly, the proposed method uses deep learning combined with domain knowledge to extract the minutiae of fingermark. Secondly, it searches and calibrates the texture region of interest (ROI). Finally, it builds a deep neural network based on residual blocks, and trains the model through Triplet Loss. The proposed method achieved an equal error rate (EER) of 0.0779 on the self-built database, which is far lower than the traditional methods. It also proves that this method can effectively reduce the labor and time costs during minutiae extraction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Paulino, A.A., Jain, A.K., Feng, J.: Latent fingerprint matching: fusion of manually marked and derived minutiae. In: 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images, pp. 63–70 (2010). https://doi.org/10.1109/SIBGRAPI.2010.17
Wang, Y.-N., Wu, Z.-D., Zhang, J.-W.: Low-quality-fingerprint identification based on convolutional neural network (Chinese). Commun. Technol. 50(06), 1276–1280 (2017)
Patil, A.R., Rahulkar, D., Modi, C.N.: Designing an efficient fingerprint recognition system for infants and toddlers. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2019). https://doi.org/10.1109/ICCCNT45670.2019.8944342
Tang, Y., Gao, F., Feng, J., Liu, Y.: FingerNet: an unified deep network for fingerprint minutiae extraction. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 108–116 (2017). https://doi.org/10.1109/BTAS.2017.8272688
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recogn. 28(11), 1657–1672 (1995)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015). https://doi.org/10.1109/CVPR.2015.7298682
Zhang, Y., Fang, S., Zhou, B., Huang, C., Li, Y.: Fingerprint match based on key minutiae and optimal statistical registration. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds.) CCBR 2014. LNCS, vol. 8833, pp. 208–215. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12484-1_23
Acknowledgement
This work was supported by the Public welfare project of Zhejiang Science and Technology Department under Grant LGF18F030008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y. et al. (2021). Signatured Fingermark Recognition Based on Deep Residual Network. 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_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-86608-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86607-5
Online ISBN: 978-3-030-86608-2
eBook Packages: Computer ScienceComputer Science (R0)