Robust hand posture recognition integrating multi-cue hand tracking

Authors:
Chuanbo Weng;Yang Li;Mingmin Zhang;Kangde Guo;Xing Tang;Zhigeng Pan
Affiliations:
State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China;State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China;State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China;State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China;Shanghai Research Center, Intel, Shanghai, China;State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
Venue:
Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
Year:
2010

Citing 9
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Abstract

This paper proposes a robust real-time method for hand tracking and hand posture recognition. Dealing with complex background, scale-invariance and rotation-invariance are the difficulties for hand posture recognition. To solve these difficulties, we firstly detect the specific posture using the method based on Modified Census Transform, in order to trigger hand tracking and hand posture recognition. For the complex background particularly with large skin-color alike objects, a multi-cue method, based on velocity weighted features and color cue, is proposed to deal with the hand tracking. Then we segment the hand using both Bayesian skin-color model and the hand tracking result. Finally, we use a novel method based on density distribution feature to recognize hand posture. It largely enforces the robustness of hand posture recognition because of scale-invariance and rotation-invariance. Experiment results and applications demonstrate the effectiveness of our method.