Thermal Imaging for Localization of Anterior Forearm Subcutaneous Veins

  • Orcan Alpar
  • Ondrej KrejcarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)


The anterior forearm recognition systems are very popular identify the subcutaneous veins, mostly by devices operating real-time. Real time projection systems are carried out by handheld devices with a near infrared (NIR) camera and a laser projector to choose venipuncture sites; however what we propose in this paper is an easier and more reliable way using infrared thermal (IR-T) camera. At the forearm some veins like Cephalic vein are mostly so concealed to detect by NIR cameras, therefore we propose an alternative method for localization of the whole vein system without preprocessing. Briefly in the thermograms, the forearm is segmented from the surrounding by crisp 2- means and the vein system is reconstructed on blank images by directional curvature method. All directional curvatures are combined by addition for merging the layers and highlighting mutual veins. The preliminary results are promising since all the vein system is revealed with invisible veins without any preprocessing.


Thermal imaging Forearm veins Directional curvature Crisp 2- means Localization Segmentation 



The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic. We are also grateful for the support of Ph.D. students of our team (Ayca Kirimtat and Pavel Blazek) in consultations regarding application aspects.


  1. 1.
    Kim, D., Kim, Y., Yoon, S., Lee, D.: Preliminary study for designing a novel vein-visualizing device. Sensors 17(2), 304–323 (2017)CrossRefGoogle Scholar
  2. 2.
    Song, J.H., Kim, C., Yoo, Y.: Vein visualization using a smart phone with multispectral Wiener estimation for point-of-care applications. IEEE J. Biomed. Health Inform. 19(2), 773–778 (2015)CrossRefGoogle Scholar
  3. 3.
    Buddharaju, P., Pavlidis, I.T.: Physiology-based face recognition in the thermal infrared spectrum. In: Medical Infrared Imaging: Principles and Practices, pp. 18.1–18.16. CRC press (2012)Google Scholar
  4. 4.
    Alpar, O., Krejcar, O.: Superficial Dorsal hand vein estimation. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017 Part I. LNCS, vol. 10208, pp. 408–418. Springer, Cham (2017). Scholar
  5. 5.
    Huang, D., Zhang, R., Yin, Y., Wang, Y., Wang, Y.: Local feature approach to dorsal hand vein recognition by centroid-based circular key-point grid and fine-grained matching. Image Vis. Comput. (2016).
  6. 6.
    Joardar, S., Chatterjee, A., Bandyopadhyay, S., Maulik, U.: Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm. Eng. Appl. Artif. Intell. 60, 151–163 (2017)CrossRefGoogle Scholar
  7. 7.
    Yun-peng, H., Zhi-yong, W., Xiao-ping, Y., Yu-ming, X.: Hand vein recognition based on the connection lines of reference point and feature point. Infrared Phys. Technol. 62, 110–114 (2014)CrossRefGoogle Scholar
  8. 8.
    Yan, X., Kang, W., Deng, F., Wu, Q.: Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing 151, 798–807 (2015)CrossRefGoogle Scholar
  9. 9.
    Lee, J.C., Lee, C.H., Hsu, C.B., Kuei, P.Y., Chang, K.C.: Dorsal hand vein recognition based on 2D Gabor filters. Imaging Sci. J. 62(3), 127–138 (2014)CrossRefGoogle Scholar
  10. 10.
    Sheetal, R.P., Goela, K.G.: Image processing in hand vein pattern recognition system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(6), 427–430 (2014)Google Scholar
  11. 11.
    Janes, R., Júnior, A.F.B.: A low cost system for Dorsal hand vein patterns recognition using curvelets. In: Proceedings of the 2014 First International Conference on Systems Informatics, Modelling and Simulation. IEEE Computer Society, pp. 47–52 (2014)Google Scholar
  12. 12.
    Sakthivel, G.: Hand vein detection using infrared light for web based account. Int. J. Comput. Appl. 112(10), 17–21 (2015)Google Scholar
  13. 13.
    Premalatha, K., Natarajan, A.M.: Hand vein pattern recognition using natural image statistics. Def. Sci. J. 65(2), 150–158 (2015)CrossRefGoogle Scholar
  14. 14.
    Srivastava, S., Bhardwaj, S., Bhargava, S.: Fusion of palm-phalanges print with palmprint and dorsal hand vein. Appl. Soft Comput. 47, 12–20 (2016)CrossRefGoogle Scholar
  15. 15.
    Lee, J.C., Lo, T.M., Chang, C.P.: Dorsal hand vein recognition based on directional filter bank. SIViP 10(1), 145–152 (2016)CrossRefGoogle Scholar
  16. 16.
    Morales-Montiel, I.I., Olvera-López, J.A., Martín-Ortíz, M., Orozco-Guillén, E.E.: Hand vein infrared image segmentation for biometric recognition. Res. Comput. Sci. 80, 55–66 (2014)Google Scholar
  17. 17.
    Miura, N., Nagasaka, A., Miyatake, T.: Extraction of finger-vein patterns using maximum curvature points in image profiles. IEEE Trans. Inf. Syst. 90(8), 1185–1194 (2007)CrossRefGoogle Scholar
  18. 18.
    Djerouni, A., Hamada, H., Loukil, A., Berrached, N.: Dorsal hand vein image contrast enhancement techniques. Int. J. Comput. Sci. 11(1), 137–142 (2014)Google Scholar
  19. 19.
    Dan, G., Guo, Z., Ding, H., Zhou, Y.: Enhancement of Dorsal hand vein image with a low-cost binocular vein viewer system. J. Med. Imaging Health Inform. 5(2), 359–365 (2015)CrossRefGoogle Scholar
  20. 20.
    Wang, Y., Duan, Q., Shark, L.K., Huang, D.: Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns. Int. J. Comput. Appl. Technol. 54(3), 151–160 (2016)CrossRefGoogle Scholar
  21. 21.
    Wang, G., Wang, J., Li, M., Zheng, Y., Wang, K.: Hand vein Image Enhancement Based on Multi-Scale Top-Hat Transform. Cybern. Inf. Technol. 16(2), 125–134 (2016)MathSciNetGoogle Scholar
  22. 22.
    Wang, J., Wang, G., Li, M., Du, W., Yu, W.: Hand vein images enhancement based on local gray-level Information histogram. Int. J. Bioautom. 19(2), 245–258 (2015)Google Scholar
  23. 23.
    Buddharaju, P., Pavlidis, I.T., Tsiamyrtzis, P., Bazakos, M.: Physiology-based face recognition in the thermal infrared spectrum. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 613–626 (2007)CrossRefGoogle Scholar
  24. 24.
    Buddharaju, P., Pavlidis, I.T., Tsiamyrtzis, P.: Physiology-based face recognition. In: IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2005, pp. 354–359. IEEE (2005)Google Scholar
  25. 25.
    Alpar, O., Krejcar, O.: Dorsal hand recognition through adaptive YCbCr imaging technique. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016 Part II. LNCS (LNAI), vol. 9876, pp. 262–270. Springer, Cham (2016). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Informatics and Management, Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec KraloveCzech Republic

Personalised recommendations