A Human-Machine Interaction Technique: Hand Gesture Recognition Based on Hidden Markov Models with Trajectory of Hand Motion

Abstract We have developed an efficient mechanism for real-time hand gesture recognition based on the trajectory of hand motion and the hidden Markov models classifier. In our system, we divide our gestures into single or both hands, one hand have been defined four basic types of directive gesture such as moving upward, downward, leftward, rightward. Then, two hands have twenty-four kinds of combination gesture. However, we apply the most natural and simple way to define eight kinds gestures in our developed human-machine interaction control system so that the users can easily operate the robot. Experimental results reveal that the face tracking rate is more than 97% in general situations and over 94% when the face suffers from temporal occlusion. The efficiency of system execution is very satisfactory, and we are encouraged to commercialize the robot in the near future.

[1]  Keun Chang Kwak,et al.  Gesture analysis for human-robot interaction , 2006, 2006 8th International Conference Advanced Communication Technology.

[2]  Xia Liu,et al.  Hand gesture recognition using depth data , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[3]  Nianjun Liu,et al.  Model structure selection & training algorithms for an HMM gesture recognition system , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[4]  Ze-Nian Li,et al.  Human Posture Recognition with Convex Programming , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[5]  Ayoub Al-Hamadi,et al.  Real-Time Capable System for Hand Gesture Recognition Using Hidden Markov Models in Stereo Color Image Sequences , 2008, J. WSCG.

[6]  Tieniu Tan,et al.  Real time hand tracking by combining particle filtering and mean shift , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[7]  S. Mitra,et al.  Gesture Recognition: A Survey , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).