Journal of Advanced Information Technology and Convergence (한국정보기술학회 영문논문지)

Korean Institute of Information Technology (한국정보기술학회)
권호 표지
DOI QR코드

DOI QR Code

PC User Authentication using Hand Gesture Recognition and Challenge-Response

  • Shin, Sang-Min (Dept. of Information Security, Mokpo National University) ;
  • Kim, Minsoo (Dept. of Information Security, Mokpo National University)
  • Received : 2018.12.09
  • Accepted : 2018.12.26
  • Published : 2018.12.31
⟨ Previous Next ⟩

Abstract

The current PC user authentication uses character password based on user's knowledge. However, this can easily be exploited by password cracking or key-logging programs. In addition, the use of a difficult password and the periodic change of the password make it easy for the user to mistake exposing the password around the PC because it is difficult for the user to remember the password. In order to overcome this, we propose user gesture recognition and challenge-response authentication. We apply user's hand gesture instead of character password. In the challenge-response method, authentication is performed in the form of responding to a quiz, rather than using the same password every time. To apply the hand gesture to challenge-response authentication, the gesture is recognized and symbolized to be used in the quiz response. So we show that this method can be applied to PC user authentication.

Keywords

References

  1. G. D. Clark and J. Lindqvist, "Engineering Gesture Based Authentication Systems", IEEE Pervasive Computing, Vol. 14, No. 1, pp.18-25,Jan.-Mar. 2015. https://doi.org/10.1109/MPRV.2015.6
  2. S. R. Cho, D.S. Choi, S.H. Jin, and H.H. Lee, "Passwordless Authentication Technology-FIDO", Electronics and Telecommunications Trends, Vol. 29, No. 4, pp. 101-109, Aug. 2014.
  3. B. Ducray, "Authentication by Gesture Recognition: a Dynamic Biometric Application", the degree of Doctor of Philosophy, University of London, 2017.
  4. Y. Cui and J. Weng, "A learning-based prediction-and-verification segmentation scheme for hand sign image sequences", IEEE Transaction on Pattern Analysis Machine Intelligence, Vol 21, No. 8, pp. 798-804, Aug. 1999. https://doi.org/10.1109/34.784311
  5. N. Neverova, C. Wolf, G. W. Taylor, and F. Nebout, "Multi-scale deep learning for gesture detection and localization", ECCVW, LNCS 8925, Zurich, Switzerland, Sep. 2014, pp. 474-490.
  6. P. Wang, W. Li, S. Liu, Z. Gao, C. Tang, and P. Ogunbona, "Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks", International Conference on Pattern Recognition(ICPR), Cancun, Mexico, Dec. 2016, Doi: 10.1109/ICPR.2016.7899599.
  7. N. Liu and B. C. Lovell, "Hand Gesture Extraction by Active Shape Models", Digital Image Computing: Techniques and Applications(DICTA), Queensland, Australia, Dec. 2005, Doi: 10.1109/DICTA.2005.41.
  8. M. S. Khasale and A. Gurjar, "Computer Control with Hand Gestures Using Ultrasonic Sensor", International Journal of Innovations & Advancement in Computer Science, Vol. 7, Issue 5, pp. 361-367, May 2018.
  9. Microsoft, "Windows Hello: Discover facial recognition on Windows 10", https://www.microsoft.com/en-us/windows/windows-hello [accessed: Dec. 01. 2018].
  10. Y. Baek, S. Lee, and J. Kim, "Design of OpenCV based Finger Recognition System using binary processing and histogram graph", Journal of the Korea Society of Computer and Information, Vol. 21, No. 2, pp. 17-23, Feb. 2016. https://doi.org/10.9708/jksci.2016.21.2.017