Efficient Hand Detection on Client-server Recognition System

Victor Chernyshov

2015

Abstract

In this paper, an efficient method for hand detection based on continuous skeletons approach is presented. It showcased real-time working speed and high detection accuracy (3-5% both FAR and FRR) on a large dataset (50 persons, 80 videos, 2322 frames). This makes the method suitable for use as a part of modern hand biometrics systems including mobile ones. Next, the study shows that continuous skeletons approach can be used as prior for object and background color models in segmentation methods with supervised learning (e.g. interactive segmentation with seeds or abounding box). This fact was successfully adopted to the developed client-server hand recognition system — both thumbnailed colored frame and extracted seeds are sent from Android application to server where Grabcut segmentation is performed. As a result, more qualitative hand shape features are extracted which is confirmed by several identification experiments. Finally, it is demonstrated that hand detection results can be used as a region of interest localization routine in the subsequent analysis of finger knuckle print. The future research will be devoted to extracting features from dorsal fingers surface and developing multi-modal classifier (hand shape and knuckle print features) for identification problem.

References

  1. Chernyshov, V. and Mestetskiy, L. (2013). Mobile machine vision system for palm-based identification. In Proceedings of the 11th International Conference ”Pattern Recognition and Image Analysis: New Information Technologies” (PRIA-11-2013), volume 2, pages 398-401.
  2. Elgammal, A. M., Muang, C., and Hu, D. (2009). Skin detection. In Encyclopedia of Biometrics, pages 1218- 1224.
  3. Fang, Y., Wang, K., Cheng, J., and Lu, H. (2007). A realtime hand gesture recognition method. In Proceedings of the 2007 International Conference on Multimedia and Expo (ICME 2007), Beijing, China, pages 995- 998. IEEE.
  4. Franzgrote, M., Borg, C., Tobias Ries, B., Büssemake, S., Jiang, X., Fieleser, M., and Zhang, L. (2011). Palmprint verification on mobile phones using accelerated competitive code. In 2011 International Conference on Hand-Based Biometrics (ICHB), pages 124-129. IEEE.
  5. Kölsch, M. and Turk, M. (2004). Robust hand detection. In In International Conference on Automatic Face and Gesture Recognition (to appear), Seoul, Korea, pages 614-619.
  6. Kozik, R. and Choras, M. (2010). Combined shape and texture information for palmprint biometrics. Journal of Information Assurance and Security, 5:60-66.
  7. Kumar, A. (2012). Can we use minor finger knuckle images to identify humans? In Proceedings of the IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS 2012), pages 55-60. IEEE.
  8. Kumar, A. and Zhang, D. (2006). Personal recognition using hand shape and texture. Trans. Img. Proc., 15(8):2454-2461.
  9. Mestetskiy, L., Bakina, I., and Kurakin, A. (2011). Hand geometry analysis by continuous skeletons. In Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II, ICIAR'11, pages 130-139, Berlin, Heidelberg. Springer-Verlag.
  10. Mestetskiy, L. and Semenov, A. (2008). Binary image skeleton-continuous approach. In Proceedings of 3rd International Conference on Computer Vision Theory and Applications (VISAPP), volume 1, pages 251- 258. INSTICC - Institute for Systems and Technologies of Information, Control and Communication.
  11. Otsu, N. (1979). A threshold selection method from graylevel histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1):62-66.
  12. Sobral, A. (2013). BGSLibrary: An opencv c++ background subtraction library. In IX Workshop de Vis?o Computacional (WVC'2013), Rio de Janeiro, Brazil.
  13. Tang, M., Gorelick, L., Veksler, O., and Boykov, Y. (2013). Grabcut in one cut. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 7813, pages 1769-1776, Washington, DC, USA. IEEE Computer Society.
  14. Vezhnevets, V., Sazonov, V., and Andreeva, A. (2003). A survey on pixel-based skin color detection techniques. In Proceedings of the GraphiCon 2003, pages 85-92.
  15. Xiao, B., Xu, X.-m., and Mai, Q.-p. (2010). Real-time hand detection and tracking using lbp features. In Cao, L., Zhong, J., and Feng, Y., editors, Advanced Data Mining and Applications, volume 6441 of Lecture Notes in Computer Science, pages 282-289. Springer Berlin Heidelberg.
  16. Yörük, E., Konukoglu, E., Sankur, B., and Darbon, J. (2006). Shape-based hand recognition. IEEE Transactions on Image Processing, 15(7):1803-1815.
  17. Zhang, L., Zhang, L., Zhang, D., and Zhu, H. (2010). Online finger-knuckle-print verification for personal authentication. Pattern Recognition, 43(7):2560-2571.
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Paper Citation


in Harvard Style

Chernyshov V. (2015). Efficient Hand Detection on Client-server Recognition System . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 461-468. DOI: 10.5220/0005315704610468


in Bibtex Style

@conference{visapp15,
author={Victor Chernyshov},
title={Efficient Hand Detection on Client-server Recognition System},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={461-468},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005315704610468},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Efficient Hand Detection on Client-server Recognition System
SN - 978-989-758-090-1
AU - Chernyshov V.
PY - 2015
SP - 461
EP - 468
DO - 10.5220/0005315704610468