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Dorsal Hand Recognition Through Adaptive YCbCr Imaging Technique

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Computational Collective Intelligence (ICCCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9876))

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Abstract

Dorsal hand recognition is a trending topic in biometrics and human computer interactive systems. The characteristic and unique shape of the dorsal side of users’ hands could be identified and discriminated for continuous authentication or could be tracked for second security option as a keyboard passwords. Therefore we propose a novel recognition system that deals with users’ hands on the keyboard using adaptive YCbCr color space. The images are extracted from a video recorded by a camera mounted on the monitor and the Cb and the Cr color intervals of the dorsal hands are identified and stored. In contrast with the common algorithms that deal with the static interval, we propose an adaptive system which initially identifies the Cb and Cr values of the users’ hands and subsequently recognize the dorsal hands throughout the frames of the video.

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References

  1. Frolova, D., Stern, H., Berman, S.: Most probable longest common subsequence for recognition of gesture character input. Cybern. IEEE Trans. 43(3), 871–880 (2013)

    Article  Google Scholar 

  2. Ghotkar, A.S., Kharate, G.K.: Vision based real time hand gesture recognition techniques for human computer interaction. Int. J. Comput. Appl. 70(16), 1–6 (2013)

    Google Scholar 

  3. Weber, H., Jung, C.R., Gelb, D.: Hand and object segmentation from RGB-D images for interaction with planar surfaces. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2984–2988. IEEE (2015)

    Google Scholar 

  4. Feng, K.P., Wan, K., Luo, N.: Natural gesture recognition based on motion detection and skin color. Appl. Mech. Mater. 321, 974–979 (2013)

    Google Scholar 

  5. Plouffe, G., Cretu, A.M., Payeur, P.: Natural human-computer interaction using static and dynamic hand gestures. In: 2015 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), pp. 1–6. IEEE (2015)

    Google Scholar 

  6. Tu, Y.J., Kao, C.C., Lin, H.Y., Chang, C.C.: Face and gesture based human computer interaction. Int. J. Sig. Process. image Process. Pattern Recogn. 8(9), 219–228 (2015)

    Google Scholar 

  7. Jeong, J., Jang, Y.: Max–min hand cropping method for robust hand region extraction in the image-based hand gesture recognition. Soft. Comput. 19(4), 815–818 (2015)

    Article  Google Scholar 

  8. Ahmad, I., Jan, Z., Shah, I.A., Ahmad, J.: Hand recognition using palm and hand geometry features. Sci. Int. 27(2), 1177–1181 (2015)

    Google Scholar 

  9. Zhang, D., Guo, Z., Gong, Y.: Dorsal hand recognition. In: Multispectral Biometrics, Springer International Publishing, pp. 165–186 (2016)

    Google Scholar 

  10. Zhang, D., Guo, Z., Gong, Y.: Comparison of Palm and Dorsal Hand Recognition. Multispectral Biometrics. Springer International Publishing, Heidelberg (2016)

    Book  Google Scholar 

  11. Zhang, D., Guo, Z., Gong, Y.: Multiple Band Selection of Multispectral Dorsal Hand. Multispectral Biometrics. Springer International Publishing, Heidelberg (2016)

    Book  Google Scholar 

  12. Qiu-yu, Z., Jun-chi, L., Mo-yi, Z., Hong-xiang, D., Lu, L.: Hand gesture segmentation method based on YCbCr color space and k-means clustering. Int. J. Signal Process. Image Process. Pattern Recogn. 8(5), 105–116 (2015)

    Google Scholar 

  13. Kaur, A., Kranthi, B.V.: Comparison between YCbCr color space and CIELab color space for skin color segmentation. IJAIS 3(4), 30–33 (2012)

    Google Scholar 

  14. Chitra, S., Balakrishnan, G.: Comparative study for two color spaces HSCbCr and YCbCr in skin color detection. Appl. Math. Sci. 6(85), 4229–4238 (2012)

    Google Scholar 

  15. Shen, X.G., Wu, W.: An algorithm of lips secondary positioning and feature extraction based on YCbCr color space. In: International Conference on Advances in Mechanical Engineering and Industrial Informatics. pp. 1472–1478. Atlantis Press (2015)

    Google Scholar 

  16. Alpar, O.: Intelligent biometric pattern password authentication systems for touchscreens. Expert Syst. Appl. 42(17), 6286–6294 (2015)

    Article  Google Scholar 

  17. Alpar, O.: Keystroke recognition in user authentication using ANN based RGB histogram technique. Eng. Appl. Artif. Intell. 32, 213–217 (2014)

    Article  Google Scholar 

  18. Alpar, O., Krejcar, O.: Biometric swiping on touchscreens. In: Saeed, K., Homenda, W. (eds.) Canadian AI 2013. LNCS, vol. 9339, pp. 193–203. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  19. Alpar, O., Krejcar, O.: Pattern password authentication based on touching location. In: Jackowski, K., et al. (eds.) IDEAL 2015. LNCS, vol. 9375, pp. 395–403. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24834-9_46

    Chapter  Google Scholar 

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Acknowledgment

This work and the contribution were supported by project “Smart Solutions for Ubiquitous Computing Environments” FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2016-2102).

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Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Kralove, Czech Republic

    Orcan Alpar & Ondrej Krejcar

Authors
  1. Orcan Alpar
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  2. Ondrej Krejcar
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Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Aristotle University of Thessaloniki , Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Manageme, Democritus University of Thrace Department of Forestry and Manageme, Orestiada Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology , Wrocław, Poland

    Bogdan Trawiński

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© 2016 Springer International Publishing Switzerland

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Alpar, O., Krejcar, O. (2016). Dorsal Hand Recognition Through Adaptive YCbCr Imaging Technique. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-45246-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45245-6

  • Online ISBN: 978-3-319-45246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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