Skip to main content
SpringerLink
Log in

Efficient hand vein recognition using local keypoint descriptors and directional gradients

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a computationally efficient palm and wrist vein biometric system through finely tuning computer-vision algorithms. In particular, a comprehensive analysis of the scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) keypoint descriptors was conducted along with a novel idea of a score-based fusion of directional image derivatives to achieve outstanding recognition results. The work demonstrates that appropriate vein image processing, keypoint extraction, optimal matching metrics, and combination of classification scores from a group of directional gradients lead to robust and stable vein recognition. It was shown through experimental analysis that the developed biometric system outperforms all state-of-the-art results other than deep learning methods on the two public hand vein databases (VERA and PUT). Moreover, an absolute 100% recognition for the PUT palm dataset was achieved without using deep learning. The proposed method is more suitable for embedded implementation compared to deep learning algorithms, with only a slight penalty in performance compared to deep learning architectures.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ahmed MA, El-Horbaty E-SM, Salem A-BM (2015) Intelligent techniques for matching palm vein images. Egyptian Computer Science Journal 39:1–14

    Google Scholar 

  2. Ananthi G, Sekar JR, Arivazhagan S (2021) Human palm vein authentication using curvelet multiresolution features and score level fusion. Vis Comput:1–14

  3. Arakala A, Hao H, Davis S, Horadam K (2015) The palm vein graph - feature extraction and matching. In: International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and and Technology Publications, pp 56–64

    Google Scholar 

  4. Babalola FO, Bitirim Y, Toygar Ö (2021) Palm vein recognition through fusion of texture-based and CNN-based methods. SIViP 15:459–466

    Article  Google Scholar 

  5. Bay H, Tuytelaars T, Van Gool L (2006) SURF: Speeded Up Robust Features. In: SURF: speeded up robust features. Springer, Berlin, Heidelberg, pp 404–417

    Google Scholar 

  6. Bouzida N, Bendada AH, Maldague XP (2010) Near-infrared image formation and processing for the extraction of hand veins. J Mod Opt 57:1731–1737. https://doi.org/10.1080/09500341003725763

    Article  MATH  Google Scholar 

  7. Bradski G, Kaehler A, Cambridge B·, et al (2008) Learning OpenCV. O’Reilly Media Inc.

    Google Scholar 

  8. Chen Y-Y, Hsia C-H, Chen P-H (2021) Contactless multispectral palm-vein recognition with lightweight convolutional neural network. IEEE Access 9:149796–149806

    Article  Google Scholar 

  9. Cho S, Oh BS, Kim D, Toh KA (2021) Palm-vein verification using images from the visible Spectrum. IEEE Access

  10. Coetzee L, Botha EC (1993) Fingerprint recognition in low quality images. Pattern Recogn 26:1441–1460. https://doi.org/10.1016/0031-3203(93)90151-L

    Article  Google Scholar 

  11. Das A, Pal U, Ballester MAF, Blumenstein M (2014) A new wrist vein biometric system. In: 2014 IEEE symposium on computational intelligence in biometrics and identity management (CIBIM). IEEE, pp 68–75

    Chapter  Google Scholar 

  12. Du G, Su F, Cai A (2009) Face recognition using SURF features. In: Ding M, Bhanu B, Wahl FM, Roberts J (eds) SPIE 7496, MIPPR 2009: pattern recognition and computer vision, 749628. International Society for Optics and Photonics, pp 749628–749634

    Google Scholar 

  13. Hartung D, Olsen MA, Xu H, Busch C (2011) Spectral minutiae for vein pattern recognition. In: 2011 international joint conference on biometrics (IJCB). IEEE, pp 1–7

    Google Scholar 

  14. Jain A, Lin H, Bolle R (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19:302–314. https://doi.org/10.1109/34.587996

    Article  Google Scholar 

  15. Jain AK, Bolle R, Pankanti S (2006) Biometrics : personal identification in networked society. Springer Science & Business Media

    Google Scholar 

  16. Jhong S-Y, Tseng P-Y, Siriphockpirom N, Hsia C-H, Huang M-S, Hua K-L, Chen Y-Y (2020) An automated biometric identification system using CNN-based palm vein recognition. In: 2020 international conference on advanced robotics and intelligent systems (ARIS). IEEE, pp 1–6

    Google Scholar 

  17. Kabaciński R, Kowalski M (2011) Vein pattern database and benchmark results. Electron Lett 47:1127–1128. https://doi.org/10.1049/el.2011.1441

    Article  Google Scholar 

  18. Kang W, Wu Q (2014) Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Transactions on Information Forensics and Security 9:1974–1985. https://doi.org/10.1109/TIFS.2014.2361020

    Article  Google Scholar 

  19. Kong WK, Zhang D, Li W (2003) Palmprint feature extraction using 2-D Gabor filters. Pattern Recogn 36:2339–2347. https://doi.org/10.1016/S0031-3203(03)00121-3

    Article  Google Scholar 

  20. Kumar R, Singh RC, Kant S (2021) Dorsal hand vein-biometric recognition using convolution neural network. In: International Conference on Innovative Computing and Communications. Springer, pp 1087–1107

    Chapter  Google Scholar 

  21. Li W, Zhang D, Xu Z (2002) Palmprint identification by fourier transform. Int J Pattern Recognit Artif Intell 16:417–432. https://doi.org/10.1142/S0218001402001757

    Article  Google Scholar 

  22. Lindeberg T (1994) Scale-space theory: a basic tool for analyzing structures at different scales. J Appl Stat 21:225–270. https://doi.org/10.1080/757582976

    Article  Google Scholar 

  23. Lowe DG (2004) Distinctive image features from scale-invariant Keypoints. Int J Comput Vis 60:91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  24. Ma X, Jing X, Huang H, Cui Y, Mu J (2017) Palm vein recognition scheme based on an adaptive Gabor filter. IET Biometrics 6:325–333

    Article  Google Scholar 

  25. Mirmohamadsadeghi L, Drygajlo A (2014) Palm vein recognition with local texture patterns. IET Biometrics 3:198–206. https://doi.org/10.1049/iet-bmt.2013.0041

    Article  Google Scholar 

  26. Nivas S, Prakash P (2014) Real-time finger-vein recognition system. International Journal of Engineering Research and General Science 2:580–591

    Google Scholar 

  27. Olsen MA, Hartung D, Busch C, Larsen R (2010) Contrast enhancement and metrics for biometric vein pattern recognition. In: International conference on intelligent computing. Springer, Berlin, Heidelberg, pp 425–434

    Google Scholar 

  28. Pan M, Kang W (2011) Palm vein recognition based on three local invariant feature extraction algorithms. In: Chinese conference on biometric recognition. Springer, Berlin, Heidelberg, pp 116–124

    Chapter  Google Scholar 

  29. Pedersen JT (2011) Study group SURF: feature detection and description. Department of Computer Science, Aarhus University

    Google Scholar 

  30. Raghavendra R, Surbiryala J, Busch C (2015) Hand dorsal vein recognition: sensor, algorithms and evaluation. In: 2015 IEEE international conference on imaging systems and techniques (IST). IEEE, pp 1–6

    Google Scholar 

  31. Sangeetha NM, Kumar TA, Natarajan DM (2014) Feature level fusion of WLBP and HOG for hand dorsal vein recognition. International Journal of Emerging Technology and Advanced Engineering 4:9–17

    Google Scholar 

  32. Shark L-K, Zhang K, Wang Y (2014) Personal identification based on multiple keypoint sets of dorsal hand vein images. IET Biometrics 3:234–245. https://doi.org/10.1049/iet-bmt.2013.0042

    Article  Google Scholar 

  33. Tome P, Marcel S (2015) Palm vein database and experimental framework for reproducible research. In: 2015 international conference of the biometrics special interest group (BIOSIG). IEEE, pp 1–7

    Google Scholar 

  34. Wang G, Wang J (2017) SIFT based vein recognition models: analysis and improvement. Computational and Mathematical Methods in Medicine 2017:1–14. https://doi.org/10.1155/2017/2373818

    Article  Google Scholar 

  35. Wang K, Zhang Y, Yuan Z, Zhuang D (2006) Hand vein recognition based on multi supplemental features of multi-classifier fusion decision. In: 2006 international conference on mechatronics and automation. IEEE, pp 1790–1795

    Chapter  Google Scholar 

  36. Wang Y, Li K, Shark L, Varley MR (2011) Hand-Dorsa Vein Recognition Based on Coded and Weighted Partition Local Binary Patterns. In: 2011 International Conference on Hand-Based Biometrics. IEEE, pp 1–5

    Google Scholar 

  37. Wu W, Elliott SJ, Lin S, Sun S, Tang Y (2019) Review of palm vein recognition. IET Biometrics 9:1–10

    Article  Google Scholar 

  38. Xueyan L, Shuxu G (2008) The fourth biometric - vein recognition. In: Pattern recognition techniques, technology and applications. InTech, pp 537–546

    Google Scholar 

  39. Zhou Y, Kumar A (2011) Human identification using palm-vein images. IEEE Transactions on Information Forensics and Security 6:1259–1274. https://doi.org/10.1109/TIFS.2011.2158423

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Kuwait University, Kuwait City, Kuwait

    Mohammad H. Alshayeji, Suood Abdulaziz Al-Roomi & Sa’ed Abed

Authors
  1. Mohammad H. Alshayeji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suood Abdulaziz Al-Roomi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sa’ed Abed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad H. Alshayeji.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alshayeji, M.H., Al-Roomi, S.A. & Abed, S. Efficient hand vein recognition using local keypoint descriptors and directional gradients. Multimed Tools Appl 81, 15687–15705 (2022). https://doi.org/10.1007/s11042-022-12608-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12608-6

Keywords

Search

Navigation