Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach | Machine Vision and Applications Skip to main content
Springer Nature Link
Log in

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Brunelli, R., Falavigna, D.: Person identification using multiple cues. IEEE Trans. Pattern Anal. Mach. Intell. 17(10), 955–966 (1995)

    Article  Google Scholar 

  2. Prakash, S., Gupta, P.: Fusion of ear with other traits. In: Ear Biometrics in 2D and 3D 107-116 ( 2015)

    Google Scholar 

  3. Shekhar, S., Patel, V.M., Nasrabadi, N.M., Chellappa, R.: Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 113–126 (2014)

    Article  Google Scholar 

  4. Yang, J., Zhang, X.: Feature-level fusion of fingerprint and finger-vein for personal identification. Pattern Recogn. Lett. 33(5), 623–628 (2012)

    Article  Google Scholar 

  5. Wang, J.G., Yau, W.Y., Suwandy, A., Sung, E.: Person recognition by fusing palmprint and palm vein images based on laplacianpalm representation. Pattern Recogn. 41(5), 1514–1527 (2008)

    Article  Google Scholar 

  6. Yang, W., Huang, X., Zhou, F., Liao, Q.: Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. Inf. Sci. 268, 20–32 (2014)

    Article  Google Scholar 

  7. Ramalho, S.M., Correia, P.L., Soares, L.D., et al.: Biometric identification through palm and dorsal hand vein patterns. In: EUROCON International Conference on Computer as a Tool, pp. 1–4 (2011)

  8. Heenaye, M., Khan, M.: A multimodal hand vein biometric based on score level fusion. Proc. Eng. 41, 897–903 (2012)

    Article  Google Scholar 

  9. Veluchamy, S., Karlmarx, L.R.: System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier. IET Biom. 6(3), 232–242 (2017)

    Article  Google Scholar 

  10. Uhl, A., Wild, P.: Personal recognition using single-sensor multimodal hand biometrics. Image and Signal Process. 5099, 396–404 (2008)

    Article  Google Scholar 

  11. Kumar, A., Zhang, D.: Combining fingerprint, palmprint and hand-shape for user authentication. In: 18th International Conference on Pattern Recognition (ICPR), vol. 4, pp. 549–552 (2006)

  12. Sanches, T., Antunes, J., Correia, P.L.: A single sensor hand biometric multimodal system. In: 15th European Signal Processing Conference, pp. 30–34 (2007)

  13. Kimura, T., Makihara, Y., Muramatsu, D., Yagi, Y.: Single sensor-based multi-quality multi-modal biometric score database and its performance evaluation. In: International Conference on Biometrics (ICB), pp. 519–526 (2015)

  14. Bhilare, S., Kanhangad, V., Chaudhari, N.: A study on vulnerability and presentation attack detection in palmprint verification system. Appl, Pattern Anal (2017). https://doi.org/10.1007/s10044-017-0606-y

    Article  MathSciNet  Google Scholar 

  15. Gottschlich, C., Marasco, E., Yang, A.Y., Cukic, B.: Fingerprint liveness detection based on histograms of invariant gradients. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 1–7 (2014)

  16. Chen, H., Valizadegan, H., Jackson, C., Soltysiak, S., Jain, A. K.: Fake hands: spoofing hand geometry systems. In: Biom. Consort (2005)

  17. Patil, I., Bhilare, S., Kanhangad, V.: Assessing vulnerability of dorsal hand-vein veryfication system to spoofing attacks using smartphone camera, In: IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1–6 (2016)

  18. Mirmohamadsadeghi, L., Drygajlo, A.: Palm vein recognition with local binary patterns and local derivative patterns. In: International Joint Conference on Biometrics (IJCB), pp. 1–6 (2011)

  19. Zhou, Y., Kumar, A.: Contactless palm vein identfication using multiple representations. In: Biometrics: Theory Applications and Systems (BTAS), pp. 1–6 (2010)

  20. Zhou, Y., Kumar, A.: Human identification using palm-vein images. IEEE Trans. Inf. Forensics Secur. 6(4), 1259–1274 (2011)

    Article  Google Scholar 

  21. Yan, X., Kang, W., Deng, F., Wu, Q.: Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing 151, 798–807 (2015)

    Article  Google Scholar 

  22. Kang, W., Wu, Q.: Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Trans. Inf. Forensics Secur. 9(11), 1974–1985 (2014)

    Article  Google Scholar 

  23. Liu, Z., Song, S.: An embedded real-time finger-vein recognition system for mobile devices. IEEE Trans. Consum. Electron. 58(2), 522–527 (2012)

    Article  Google Scholar 

  24. Yang, G., Xi, X., Yin, Y.: Finger vein recognition based on a personalized best bit map. Sensors 12(2), 1738–1757 (2012)

    Article  Google Scholar 

  25. 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(4), 194–203 (2004)

    Article  Google Scholar 

  26. 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 (ICPR), pp. 1269–1272 (2010)

  27. Song, W., Kim, T., Kim, H.C., Choi, J.H., Kong, H.J., Lee, S.R.: A finger-vein verification system using mean curvature. Pattern Recogn. Lett. 32(11), 1541–1547 (2011)

    Article  Google Scholar 

  28. Wang, Y., Xie, W., Yu, X., Shark, L.K.: An automatic physical access control system based on hand vein biometric identification. IEEE Trans. Consum. Electron. 61(3), 320–327 (2015)

    Article  Google Scholar 

  29. Huang, D., Tang, Y., Wang, Y., Chen, L., Wang, Y.: Hand vein recognition based on oriented gradient maps and local feature matching. In: Asian Conference on Computer Vision (ACCV), pp. 430–444 (2012)

    Chapter  Google Scholar 

  30. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

  31. Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900 (2015)

  32. Qin, H., El-Yacoubi, M.A.: Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Trans. Inf. Forensics Secur. 12(8), 1816–1829 (2017)

    Article  Google Scholar 

  33. Jaswal, G., Nigam, A., Nath, R.: DeepKnuckle: revealing the human identity. Multimed. Tools Appl. 76(18), 18955–18984 (2017)

    Article  Google Scholar 

  34. Heikkila, M., Pietikainen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: ICVGIP, pp. 58–69 (2006)

  35. Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Deepmatching: hierarchical deformable dense matching. Int. J. Comput. Vis. 120(3), 300–323 (2016)

    Article  MathSciNet  Google Scholar 

  36. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  37. Kanhangad, V., Kumar, A., Zhang, D.: A unified framework for contactless hand verification. IEEE Trans. Inf. Forensics Secur. 6(3), 1014–1027 (2011)

    Article  Google Scholar 

  38. Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)

    Article  Google Scholar 

  39. Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recogn. 38(12), 2270–2285 (2005)

    Article  Google Scholar 

  40. Kang, W., Liu, Y., Wu, Q., Yue, X.: Contact-free palm-vein recognition based on local invariant features. PLoS ONE 9(5), e97548 (2014)

    Article  Google Scholar 

  41. Zhang, D., Guo, Z., Lu, G., Zhang, L., Zuo, W.: An online system of multispectral palmprint verification. IEEE Trans. Instrum. Meas. 59(2), 480–490 (2010)

    Article  Google Scholar 

  42. Tome, P., Marcel, S.: On the vulnerability of palm vein recognition to spoofing attacks. In: The 8th IAPR International Conference on Biometrics (ICB) (2015)

  43. CASIA, M.: Palmprint v1 database [online]. http://www.cbsr.ia.ac.cn/mspalmprint. Accessed Mar 2017

  44. Choi, J.H., Song, W., Kim, T., Lee, S.R., Kim, H.C.: Finger vein extraction using gradient normalization and principal curvature. In: SPIE-IS&T Electronic Imaging (2009)

  45. Arandjelovific, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: Computer Vision and Pattern Recognition (CVPR), pp. 2911–2918 (2012)

Download references

Acknowledgements

This work was supported in part by CSIR-INDIA under grant no. 22(0697)/15/EMR-II.

Author information

Authors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Shruti Bhilare & Vivek Kanhangad

  2. Indian Institute of Technology Mandi, Mandi, India

    Gaurav Jaswal & Aditya Nigam

Authors
  1. Shruti Bhilare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaurav Jaswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vivek Kanhangad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aditya Nigam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shruti Bhilare.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhilare, S., Jaswal, G., Kanhangad, V. et al. Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach. Machine Vision and Applications 29, 1269–1286 (2018). https://doi.org/10.1007/s00138-018-0959-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0959-2

Keywords

Search

Navigation