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Veintr: robust end-to-end full-hand vein identification with transformer

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Abstract

Hand vein identification stands out to be an increasingly popular approach for biometric identification due to its distinctiveness and convenience. While state-of-the-art techniques are able to achieve good performance, they share two common drawbacks: (1) complex preprocessing procedures, e.g., vein enhancement and Region of Interest (ROI) extraction, and (2) vein information loss due to hand ROI partition. To address these issues, we propose VeinTr, an end-to-end full-hand vein identification approach. In particular, our VeinTr consists of three components: a local feature extractor, a lightweight transformer, and a global feature decoder. We first obtain local features via convolution-based ResNet-like blocks. Then the attention mechanism is employed to aggregate global features from local features, which can be then decoded as global hand vein features. Finally, a global feature decoder is applied to generate robust hand features. By doing so, VeinTr is capable of directly extracting robust hand vein features from raw hand vein images. We evaluate our method on CASIA, TPV, and PLUSVein hand vein datasets. Experimental results show that our approach outperforms the state-of-the-art methods and has strong inter-dataset generalization abilities.

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Data Availability

No datasets were generated during the current study.

References

  1. Jing, Y., Xuequan L., Shang G.: 3D face recognition: a comprehensive survey in 2022. Comput. Vis. Media 9(4), 657–685 (2023)

  2. Zeng, S., Xiong, Y.: Weighted average integration of sparse representation and collaborative representation for robust face recognition, Computational Visual. Media 2, 357–365 (2016)

    Google Scholar 

  3. Feng, D., Lu, X., Lin, X.: Deep detection for face manipulation. In: Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part V, vol. 27, pp. 316–323. Springer (2020)

  4. Fung, S., Lu, X., Zhang, C., Li, C.-T., Deepfakeucl: deepfake detection via unsupervised contrastive learning. In: International Joint Conference on Neural Networks (IJCNN), vol. 2021, pp. 1–8. IEEE (2021)

  5. Wu, W., Elliott, S.J., Lin, S., Sun, S., Tang, Y.: Review of palm vein recognition. IET Biometr. 9, 1–10 (2020)

    Article  Google Scholar 

  6. Huang, B., Dai, Y., Li, R., Tang, D., Li, W.: Finger-vein authentication based on wide line detector and pattern normalization. In: 20th International Conference on Pattern Recognition, vol. 2010, pp. 1269–1272. IEEE (2010)

  7. Zhou, Y., Kumar, A.: Human identification using palm-vein images. IEEE Trans. Inf. Forens. Secur. 6, 1259–1274 (2011)

    Article  Google Scholar 

  8. Wu, K.-S., Lee, J.-C., Lo, T.-M., Chang, K.-C., Chang, C.-P.: A secure palm vein recognition system. J. Syst. Softw. 86, 2870–2876 (2013)

    Article  Google Scholar 

  9. Wirayuda, T.A.B.: Palm vein recognition based-on minutiae feature and feature matching. In: 2015 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 350–355. IEEE (2015)

  10. Ananthi, G., Raja Sekar, J., Arivazhagan, S.: Human palm vein authentication using curvelet multiresolution features and score level fusion. Vis. Comput. 1–14 (2022)

  11. Mirmohamadsadeghi, L., Drygajlo, A.: Palm vein recognition with local texture patterns. Iet Biometr. 3, 198–206 (2014)

    Article  Google Scholar 

  12. Kang, W., Wu, Q.: Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Trans. Inf. Forens. Secur. 9, 1974–1985 (2014)

    Article  Google Scholar 

  13. Pratiwi, A.Y., Budi, W.T.A., Ramadhani, K.N.: Identity recognition with palm vein feature using local binary pattern rotation invariant. In: 2016 4th International Conference on Information and Communication Technology (ICoICT), pp. 1–6. IEEE (2016)

  14. Piciucco, E., Maiorana, E., Campisi, P.: Palm vein recognition using a high dynamic range approach. Iet Biometr. 7, 439–446 (2018)

    Article  Google Scholar 

  15. Fronitasari, D., Gunawan, D.: Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature. In: 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, pp. 18–22. IEEE (2017)

  16. Bhilare, S., Jaswal, G., Kanhangad, V., Nigam, A.: Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach. Mach. Vis. Appl. 29, 1269–1286 (2018)

    Article  Google Scholar 

  17. Thapar, D., Jaswal, G., Nigam, A., Kanhangad, V., Pvsnet: Palm vein authentication siamese network trained using triplet loss and adaptive hard mining by learning enforced domain specific features. In: IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), vol. 2019, pp. 1–8. IEEE (2019)

  18. Chen, Y.-Y., Jhong, S.-Y., Hsia, C.-H., Hua, K.-L.: Explainable AI: a multispectral palm-vein identification system with new augmentation features. ACM Trans. Multimedia Comput., Commun., Appl. (TOMM) 17, 1–21 (2021)

    Article  Google Scholar 

  19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  20. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

  21. Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: EAPT: efficient attention pyramid transformer for image processing. IEEE Trans. Multimedia (2021)

  22. Öztürk, H.İ., Selbes, B., Artan, Y.: Minnet: minutia patch embedding network for automated latent fingerprint recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1627–1635 (2022)

  23. Zhang, Y., Zhao, R., Zhao, Z., Ramakrishnan, N., Aggarwal, M., Medioni, G., Ji, Q.: Robust partial fingerprint recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1011–1020 (2023)

  24. Kolberg, J., Priesnitz, J., Rathgeb, C., Busch, C.: Colfispoof: a new database for contactless fingerprint presentation attack detection research. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 653–661 (2023)

  25. Johnson, J., Chitra, R.: Multimodal biometric identification based on overlapped fingerprints, palm prints, and finger knuckles using BM-KMA and CS-RBFNN techniques in forensic applications. Vis. Comput. 1–15 (2023)

  26. Ito, K., Sato, T., Aoyama, S., Sakai, S., Yusa, S., Aoki, T.: Palm region extraction for contactless palmprint recognition. In: 2015 International Conference on Biometrics (ICB), pp. 334–340. IEEE (2015)

  27. Gumaei, A., Sammouda, R., Al-Salman, A.M., Alsanad, A.: An effective palmprint recognition approach for visible and multispectral sensor images. Sensors 18, 1575 (2018)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  28. Genovese, A., Piuri, V., Scotti, F., Vishwakarma, S.: Touchless palmprint and finger texture recognition: a deep learning fusion approach. In: 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–6. IEEE (2019)

  29. Chai, T., Prasad, S., Yan, J., Zhang, Z.: Contactless palmprint biometrics using DeepNet with dedicated assistant layers. Vis. Comput. 39(9), 4029–4047 (2023)

    Article  Google Scholar 

  30. Li, X., Guo, S., Gao, F., Li, Y.: Vein pattern recognitions by moment invariants. In: 2007 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 612–615. IEEE (2007)

  31. Akbar, A.F., Wirayudha, T.A.B., Sulistiyo, M.D.: Palm vein biometric identification system using local derivative pattern. In: 2016 4th International Conference on Information and Communication Technology (ICoICT), pp. 1–6. IEEE (2016)

  32. Kang, W., Liu, Y., Wu, Q., Yue, X.: Contact-free palm-vein recognition based on local invariant features. PLoS One 9, e97548 (2014)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  33. Rahul, R.C., Cherian, M., Mohan, M.: A novel MF-LDTP approach for contactless palm vein recognition. In: 2015 International Conference on Computing and Network Communications (CoCoNet), pp. 793–798. IEEE (2015)

  34. Wu, W., Elliott, S.J., Lin, S., Yuan, W.: Low-cost biometric recognition system based on NIR palm vein image. IET Biometr. 8, 206–214 (2019)

    Article  Google Scholar 

  35. Perwira, D.Y., Agung, B.T., Sulistiyo, M.D.: Personal palm vein identification using principal component analysis and probabilistic neural network. In: 2014 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 99–104. IEEE (2014)

  36. Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Deepmatching: hierarchical deformable dense matching. Int. J. Comput. Vis. 120, 300–323 (2016)

    Article  MathSciNet  Google Scholar 

  37. Qin, H., El-Yacoubi, M.A.: Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Trans. Inf. Forens. Secur. 12, 1816–1829 (2017)

    Article  Google Scholar 

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

    Article  ADS  Google Scholar 

  39. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)

  40. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

  41. Hao, Y., Sun, Z., Tan, T., Ren, C.: Multispectral palm image fusion for accurate contact-free palmprint recognition. In: 2008 15th IEEE International Conference on Image Processing, pp. 281–284. IEEE (2008)

  42. Zhang, L., Cheng, Z., Shen, Y., Wang, D.: Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset. Symmetry 10, 78 (2018)

    Article  ADS  Google Scholar 

  43. Kauba, C., Prommegger, B., Uhl, A.: Combined fully contactless finger and hand vein capturing device with a corresponding dataset. Sensors 19, 5014 (2019)

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Acknowledgements

This work is supported in part by the investigator fund (3.2501.11.47) and the industry fund (3.6267.01).

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Author notes

  1. Shenglin Lu and Sheldon Fung contributed equally.

Authors and Affiliations

  1. OPT MV, Dongguan, China

    Shenglin Lu & Wei Pan

  2. La Trobe University, Melbourne, VIC, Australia

    Sheldon Fung, Nilmini Wickramasinghe & Xuequan Lu

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  1. Shenglin Lu
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  2. Sheldon Fung
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  3. Wei Pan
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  4. Nilmini Wickramasinghe
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  5. Xuequan Lu
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Contributions

Shenglin Lu and Sheldon Fung did the experiments and wrote the manuscript text. Wei Pan, Nilmini Wickramasinghe, and Xuequan Lu helped with the experiment and refining the text and figures. All authors reviewed the manuscript.

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Correspondence to Xuequan Lu.

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Lu, S., Fung, S., Pan, W. et al. Veintr: robust end-to-end full-hand vein identification with transformer. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03286-6

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