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Application of Artificial Neural Networks in the Human Identification Based on Thermal Image of Hands

  • Tomasz WalczakEmail author
  • Jakub Krzysztof Grabski
  • Martyna Michałowska
  • Dominika Szadkowska
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 831)

Abstract

The aim of this study was to check the possibility of identifying the persons based on the properties of thermal maps and a temperature distribution of a hand, obtained from a thermal image, with use of artificial neural networks. For this purpose, a series of thermographs of the right hand of eight people was taken, with a thermal imaging camera. The photos were taken under the same thermal conditions, but with different state of warming of hands. After processing the photos (determining the edges, characteristic hand points and areas of interest), the parameters characterizing the metacarpal temperature distribution were determined. Eight parameters were chosen, which were average temperatures of the areas of interest. These parameters were input data of neural networks in the learning and identification process. As it was shown in this study, these parameters were sufficient to clearly identify the persons. Neural networks, designed as multi-layered perceptron, after proper learning showed very high values of identification parameters, including high values of sensitivity and specificity, what proves the high quality of classification. Such identification is possible with the natural thermal state of the hand and if thermal images are not strongly disturbed, the artificial neural networks are very good tool to implement in persons identification process.

Keywords

Human identification Neural networks Thermography 

Notes

Acknowledgment

The work was funded by the grant 02/21/DSPB/3493 from the Ministry of Higher Education and Science, Poland.

During the realization of this work Dr. Jakub K. Grabski was supported with scholarship funded by the Foundation for Polish Science (FNP).

References

  1. 1.
    Badawi, A.: Hand vein biometric verification prototype: a testing performance and patterns similarity. In: Proceedings of the 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2006, Las Vegas, Nevada, USA (2006)Google Scholar
  2. 2.
    Czajka, A., Bulwan, P.: Biometrics verification based on hand thermal images. In: Proceedings of the 6th International Conference on Biometrics, ICB 2013, Madrid, Spain (2013)Google Scholar
  3. 3.
    Fan, K., Lin, C.: The using of thermal images of palm-dorsa vein-patterns for biometric verification. IEEE Trans. Circ. Syst. Video Technol. 14(2), 199–213 (2004)CrossRefGoogle Scholar
  4. 4.
    Grabski, J.K., Walczak, T., Michałowska, M., Cieślak, M.: Gender recognition using artificial neural networks and data coming from force plates, innovations in biomedical engineering. In: Gzik, M. et al. (eds.) IBE 2017. Advances in Intelligent Systems and Computing, vol. 623, pp. 53–60. Springer, Cham (2018)Google Scholar
  5. 5.
    Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer, Norwell (2006)Google Scholar
  6. 6.
    Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer, New York (2008)CrossRefGoogle Scholar
  7. 7.
    Kumar, A., Hanmandlu, M., Madasu, V.K., Lovell, B.C.: Biometric authentication based on infrared thermal hand vein patterns. Digit. Image Comput. Tech. Appl. 2008, 331–338 (2009)Google Scholar
  8. 8.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)CrossRefGoogle Scholar
  9. 9.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  10. 10.
    Walczak, T., Grabski, J.K., Grajewska, M., Michałowska, M.: The recognition of human by the dynamic determinants of the gait with use of ANN. In: Awrejcewicz, J. (ed.) Springer Proceedings in Mathematics and Statistics, Dynamical Systems: Modelling, vol. 181, pp. 375–385. Springer, Cham (2016)Google Scholar
  11. 11.
    Wang, L., Leedham, G.: A thermal hand vein pattern verification system. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, vol. 3687, pp. 58–65. Springer, Berlin, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Wang, M.H.: Hand recognition using thermal image and extension neural network. Math. Prob. Eng. 2012, 15 (2012)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Xiao, Q.: A note on computational intelligence methods in biometrics. Int. J. Biometr. 4(2), 180–188 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Applied Mechanics, Faculty of Mechanical Engineering and ManagementPoznan University of TechnologyPoznanPoland

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