Hand Gesture Recognition using Optical Flow Field Segmentation and Boundary Complexity Comparison based on Hidden Markov Models

  • Park, Sang-Yun (Department of Information Communication Engineering, TongMyong University) ;
  • Lee, Eung-Joo (Department of Information Communication Engineering, TongMyong University)
  • Received : 2010.10.06
  • Accepted : 2011.03.02
  • Published : 2011.04.30


In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.


Supported by : NIPA(National IT Industry Promotion Agency)


  1. Bai yu and Eung-Joo Lee, "The hand mouse: Hand detection and hand tracking," International Conference on Multimedia, Information Technology and its Application 2009(MITA 2009), pp. 244-245, 19-21 Aug. 2009.
  2. Bai yu, Sang-Yun Park, and Eung-Joo Lee, "Hand Posture Recognition Based on SVM Using on Mobile Phone," Conference on Korea Multimedia Society 2009, Vol.12, No.2, pp. 619-620, Nov. 2009.
  3. Eng-Jon Ong and Richard Bowden, "A boosted classifier tree for hand shape detection," in Proceedings of Int. Conf. on Automatic Face and Gesture Recognition. Seoul, Korea, pp. 889-894, May 2004.
  4. Mathias Kolsch and Matthew Turk, "Robust hand detection," in Proceedings of Int. Conf. on Automatic Face and Gesture Recognition. Seoul, Korea, pp. 614-619, May 2004.
  5. Lars Bretzner, Ivan Laptev, and Tony Lindeberg, "Hand gesture recognition using multi- scale colour features, hierarchical models and particle filtering," in Proceedings of Int. Conference on Automatic Face and Gesture Recognition. Washington D.C., pp. 423-428, May 2002.
  6. P. Viola and M Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of Computer Vision and Pattern Recognition. Hawaii, U.S. pp. 511-518, 2001.
  7. Nagel H.H, "Displacement Vectors Derived from Second-order Intensity Variations in Image Sequences," [J]. Computer Vision Graph Image Process, pp. 85-117, 1983.
  8. Nagel H.H and Enkermann W, "An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences," [J]. IEEE Trans, PAMI-5, pp. 565-593, 1986.
  9. HornB K.P and Schunck B.G, "Determining Optical Flow," [J]. Artificial Intelligence, No.17, pp. 185-203, 1981.
  10. P. Peer, J. Kovac, and F. Solina, "Human skin color clustering for face detection," In submitted to EUROCON 2003-International Conference on Computer as a Tool, 2003.
  11. Sang-Yun Park and Eung-Joo Lee, "Hand Gesture Recognition Algorithm Robust to Complex Image," Journal of Korea Multimedia Society 2010, Vol.13, No.7, pp. 1000-1015, July. 2010.
  12. W. Sharbek and A. Koshan, "Color Image Segmentation-a survey-." Tech. Rep. Institute for Technical Informatics. Technical University of Berlin, October, 1994.
  13. E. Person and K.S. Fu, "Shape Discrimination using Fourier Descriptor," IEEE Transactions SMC, Vol. No.3, pp. 170-179, 1977.
  14. D. Shridhar and A. Badreldin, "High-Accuracy Character Recognition Algorithm using Fourier and Topology Descriptor," Pattern Recognition Vol.17 pp. 515-524, 1984.
  15. Li wei and Eung-Joo Lee, "An IP-TV Remote Controller Based on Hand Posture Recognition Using Fourier Descriptor," Conference on Korea Multimedia Society 2009, Vol.12, No.2, pp. 510-511, Nov. 2009.
  16. A. Kundu and Y. He, P. Bahl, "Recognition of Handwritten Word: First and Second Order Hidden Markov Model Based Approach," Pattern Recognition Vol.22, No.3, pp. 283-301, 1989.
  17. Y. Yamato, J. Ohya. and K. Ishii, "Recognizing Human Action in Time-Sequential Images Using Hidden Markov Models," Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 379-385, 1992.
  18. J. Schlenzig, E. Hunter. and R. Jain, "Recursive Identification of Gesture Inputs Using Hidden Markov Models," Proceedings Second Annual Conference on Applications of Computer Vision, pp. 184-194, 1994.

Cited by

  1. Face Tracking System Using Updated Skin Color vol.18, pp.5, 2015,