Skip to main content
SpringerLink
Log in

Contrastive Learning-Based Finger-Vein Recognition with Automatic Adversarial Augmentation

  • Conference paper
  • First Online:
Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2023)

  • 133 Accesses

Abstract

In finger-vein recognition tasks, obtaining large labeled datasets for supervised deep learning is often difficult. To address this challenge, self-supervised learning (SSL) provides a solution by first pre-training a neural network using unlabeled data and subsequently fine-tuning it for downstream tasks. Contrastive learning, a variant of SSL, enables effective learning of image-level representations. To address the issue of insufficient labeled data for vein feature extraction and classification, we propose CL3A-FV, a Contrastive Learning-based Finger-Vein image recognition approach with Automatic Adversarial Augmentation in this paper. Specifically, CL3A-FV consists of the dual-branch augmentation network, Siamese encoder, discriminator, and distributor. The training process involves two steps: 1) training the Siamese encoder by updating its parameters while keeping other components fixed; and 2) training the dual-branch augmentation network with a fixed Siamese encoder, integrating a discriminator to distinguish views generated by the two branches, and a distributor to constrain the distribution of the augmented data. Both networks are updated adversarially using the stochastic gradient descent. We conduct extensive experiments to evaluate CL3A-FV on three finger-vein datasets, and the experimental results show that the proposed CL3A-FV achieves significant improvements compared to traditional self-supervised learning techniques and supervised methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Shaheed, K., Liu, H., Yang, G., Qureshi, I., Gou, J., Yin, Y.: A systematic review of finger vein recognition techniques. Information 9(9), 213 (2018)

    Article  Google Scholar 

  2. Yang, J., Zhang, X.: Feature-level fusion of global and local features for finger-vein recognition. In: IEEE 10th International Conference On Signal Processing Proceedings, pp. 1702–1705. IEEE (2010)

    Google Scholar 

  3. Liu, Y., Ling, J., Liu, Z., Shen, J., Gao, C.: Finger vein secure biometric template generation based on deep learning. Soft. Comput. 22, 2257–2265 (2018)

    Article  Google Scholar 

  4. Zhang, D., Zuo, W., Yue, F.: A comparative study of palmprint recognition algorithms. ACM Comput. Surv. (CSUR) 44(1), 1–37 (2012)

    Article  Google Scholar 

  5. Yang, L., Yang, G., Wang, K., Hao, F., Yin, Y.: Finger vein recognition via sparse reconstruction error constrained low-rank representation. IEEE Trans. Inf. Forensics Secur. 16, 4869–4881 (2021)

    Article  Google Scholar 

  6. Miura, N., Nagasaka, A., Miyatake, T.: Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 15, 194–203 (2004)

    Article  Google Scholar 

  7. Miura, N., Nagasaka, A., Miyatake, T.: Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans. Inf. Syst. 90(8), 1185–1194 (2007)

    Article  Google Scholar 

  8. Lee, E.C., Jung, H., Kim, D.: New finger biometric method using near infrared imaging. Sensors 11(3), 2319–2333 (2011)

    Article  Google Scholar 

  9. Shaheed, K., et al.: Ds-cnn: a pre-trained xception model based on depth-wise separable convolutional neural network for finger vein recognition. Expert Syst. Appl. 191, 116288 (2022)

    Article  Google Scholar 

  10. Huang, J., Zheng, A., Shakeel, M.S., Yang, W., Kang, W.: Fvfsnet: frequency-spatial coupling network for finger vein authentication. IEEE Trans. Inf. Forensics Secur. 18, 1322–1334 (2023)

    Article  Google Scholar 

  11. Yang, L., Liu, X., Yang, G., Wang, J., Yin, Y.: Small-area finger vein recognition. IEEE Trans. Inf. Forensics Secur. 18, 1914–1925 (2023)

    Article  Google Scholar 

  12. Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271–21284 (2020)

    Google Scholar 

  13. Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15750–15758 (2021)

    Google Scholar 

  14. Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: International Conference on Machine Learning, pp. 12310–12320. PMLR (2021)

    Google Scholar 

  15. Zhang, J., Ma, K.: Rethinking the augmentation module in contrastive learning: learning hierarchical augmentation invariance with expanded views. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16650–16659 (2022)

    Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (2012)

    Google Scholar 

  17. Kim, W., Song, J.M., Park, K.R.: Multimodal biometric recognition based on convolutional neural network by the fusion of finger-vein and finger shape using near-infrared (nir) camera sensor. Sensors 18(7), 2296 (2018)

    Article  Google Scholar 

  18. Xie, C., Kumar, A.: Finger vein identification using convolutional neural network and supervised discrete hashing. Pattern Recogn. Lett. 119, 148–156 (2019)

    Article  Google Scholar 

  19. Fang, Z.M., Lu, Z.M.: Deep belief network based finger vein recognition using histograms of uniform local binary patterns of curvature gray images. Inter. J. Innovative Comput. Inform. Control 15(5), 1701–1715 (2019)

    Google Scholar 

  20. Li, J., Fang, P.: Fvgnn: a novel gnn to finger vein recognition from limited training data. In: 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 144–148. IEEE (2019)

    Google Scholar 

  21. Huang, Z., Guo, C.: Robust finger vein recognition based on deep cnn with spatial attention and bias field correction. Int. J. Artif. Intell. Tools 30(01), 2140005 (2021)

    Article  Google Scholar 

  22. Huang, J., Tu, M., Yang, W., Kang, W.: Joint attention network for finger vein authentication. IEEE Trans. Instrum. Meas. 70, 1–11 (2021)

    Google Scholar 

  23. Qin, H., El-Yacoubi, M.A.: Deep representation for finger-vein image-quality assessment. IEEE Trans. Circuits Syst. Video Technol. 28(8), 1677–1693 (2017)

    Article  Google Scholar 

  24. Fang, Y., Wu, Q., Kang, W.: A novel finger vein verification system based on two-stream convolutional network learning. Neurocomputing 290, 100–107 (2018)

    Article  Google Scholar 

  25. Nguyen, D.T., Yoon, H.S., Pham, T.D., Park, K.R.: Spoof detection for finger-vein recognition system using nir camera. Sensors 17(10), 2261 (2017)

    Article  Google Scholar 

  26. Hou, B., Yan, R.: Convolutional auto-encoder based deep feature learning for finger-vein verification. In: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5. IEEE (2018)

    Google Scholar 

  27. Kamaruddin, N.M., Rosdi, B.A.: A new filter generation method in pcanet for finger vein recognition. IEEE Access 7, 132966–132978 (2019)

    Article  Google Scholar 

  28. Ma, N., Li, Y., Wang, Y., Ma, S., Lu, H.: Research on roi extraction algorithm for finger vein recognition based on capsule neural network. In: International Conference on Frontiers of Electronics, Information and Computation Technologies, pp. 1–5 (2021)

    Google Scholar 

  29. El-Rahiem, B.A., El-Samie, F.E.A., Amin, M.: Multimodal biometric authentication based on deep fusion of electrocardiogram (ecg) and finger vein. Multimedia Syst. 28(4), 1325–1337 (2022)

    Article  Google Scholar 

  30. Zhu, C., Yang, Y., Jang, Y.: Research on denoising of finger vein image based on deep convolutional neural network. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 374–378. IEEE (2019)

    Google Scholar 

  31. Guo, X.J., Li, D., Zhang, H.G., Yang, J.F.: Image restoration of finger-vein networks based on encoder-decoder model. Optoelectronics Lett. 15(6), 463–467 (2019)

    Article  Google Scholar 

  32. Yang, S., Qin, H., Liu, X., Wang, J.: Finger-vein pattern restoration with generative adversarial network. IEEE Access 8, 141080–141089 (2020)

    Article  Google Scholar 

  33. He, J., et al.: Finger vein image deblurring using neighbors-based binary-gan (nb-gan). IEEE Trans. Emerging Topics Comput. Intell. (2021)

    Google Scholar 

  34. Choi, J., Hong, J.S., Owais, M., Kim, S.G., Park, K.R.: Restoration of motion blurred image by modified deblurgan for enhancing the accuracies of finger-vein recognition. Sensors 21(14), 4635 (2021)

    Article  Google Scholar 

  35. Lei, L., Xi, F., Chen, S.: Finger-vein image enhancement based on pulse coupled neural network. IEEE Access 7, 57226–57237 (2019)

    Google Scholar 

  36. Zeng, J., Wang, F., Qin, C., Gan, J., Zhai, Y., Zhu, B.: A novel method for finger vein segmentation. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds.) ICIRA 2019. LNCS (LNAI), vol. 11741, pp. 589–600. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27532-7_52

    Chapter  Google Scholar 

  37. Zeng, J.: Real-time segmentation method of lightweight network for finger vein using embedded terminal technique. IEEE Access 9, 303–316 (2020)

    Article  Google Scholar 

  38. Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018)

    Google Scholar 

  39. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  40. Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 776–794. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_45

    Chapter  Google Scholar 

  41. Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

  42. Ho, D., Liang, E., Chen, X., Stoica, I., Abbeel, P.: Population based augmentation: efficient learning of augmentation policy schedules. In: International Conference on Machine Learning, pp. 2731–2741. PMLR (2019)

    Google Scholar 

  43. Lim, S., Kim, I., Kim, T., Kim, C., Kim, S.: Fast autoaugment. In: Advances in Neural Information Processing Systems 32 (2019)

    Google Scholar 

  44. Hataya, R., Zdenek, J., Yoshizoe, K., Nakayama, H.: Faster autoaugment: learning augmentation strategies using backpropagation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 1–16. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_1

    Chapter  Google Scholar 

  45. Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702–703 (2020)

    Google Scholar 

  46. Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)

  47. Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. In: Advances in Neural Information Processing Systems 32 (2019)

    Google Scholar 

  48. Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: Advances in Neural Information Processing Systems 27 (2014)

    Google Scholar 

  49. Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems 28 (2015)

    Google Scholar 

  50. Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A.L., Le, Q.V.: Adversarial examples improve image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 819–828 (2020)

    Google Scholar 

  51. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)

  52. Raghunathan, A., Xie, S.M., Yang, F., Duchi, J.C., Liang, P.: Adversarial training can hurt generalization. arXiv preprint arXiv:1906.06032 (2019)

  53. Tang, Z., Gao, Y., Karlinsky, L., Sattigeri, P., Feris, R., Metaxas, D.: OnlineAugment: online data augmentation with less domain knowledge. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 313–329. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_19

    Chapter  Google Scholar 

  54. Yang, W., Luo, W., Kang, W., Huang, Z., Wu, Q.: Fvras-net: an embedded finger-vein recognition and antispoofing system using a unified cnn. IEEE Trans. Instrum. Meas. 69(11), 8690–8701 (2020)

    Article  Google Scholar 

  55. Das, R., Piciucco, E., Maiorana, E., Campisi, P.: Convolutional neural network for finger-vein-based biometric identification. IEEE Trans. Inf. Forensics Secur. 14(2), 360–373 (2018)

    Article  Google Scholar 

  56. Qin, H., El-Yacoubi, M.A., Li, Y., Liu, C.: Multi-scale and multi-direction gan for cnn-based single palm-vein identification. IEEE Trans. Inf. Forensics Secur. 16, 2652–2666 (2021)

    Article  Google Scholar 

  57. Shen, J., et al.: Finger vein recognition algorithm based on lightweight deep convolutional neural network. IEEE Trans. Instrum. Meas. 71, 1–13 (2021)

    Google Scholar 

  58. Kumar, A., Zhou, Y.: Human identification using finger images. IEEE Trans. Image Process. 21(4), 2228–2244 (2011)

    Article  MathSciNet  Google Scholar 

  59. Lu, Y., Xie, S.J., Yoon, S., Yang, J., Park, D.S.: Robust finger vein roi localization based on flexible segmentation. Sensors 13(11), 14339–14366 (2013)

    Article  Google Scholar 

  60. Qin, H., Hu, R., El-Yacoubi, M.A., Li, Y., Gao, X.: Local attention transformer-based full-view finger-vein identification. IEEE Trans. Circuits Syst. Video Technol. 33(6), 2767–2782 (2023)

    Article  Google Scholar 

  61. Kolesnikov, A., Zhai, X., Beyer, L.: Revisiting self-supervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1920–1929 (2019)

    Google Scholar 

  62. Peng, P., Wang, J.: How to fine-tune deep neural networks in few-shot learning? arXiv preprint arXiv:2012.00204 (2020)

  63. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

Download references

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grants 62072061 and 61976030, and in part by the Funds for Creative Research Groups of Chongqing Municipal Education Commission under Grant CXQT21034.

Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Shaojiang Deng, Huaxiu Luo & Yantao Li

  2. Chongqing Technology and Business University, Chongqing, China

    Huafeng Qin

Authors
  1. Shaojiang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huaxiu Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huafeng Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yantao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yantao Li .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool, Suzhou, China

    Xinheng Wang

  3. University of Peloponnese, Patra, Greece

    Nikolaos Voros

Rights and permissions

Reprints and permissions

Copyright information

© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deng, S., Luo, H., Qin, H., Li, Y. (2024). Contrastive Learning-Based Finger-Vein Recognition with Automatic Adversarial Augmentation. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-54528-3_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54527-6

  • Online ISBN: 978-3-031-54528-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation