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Basing on the Density Distribution Feature method, present a new hand recognition method according to color space which could recognize hand in real time vision. First, establish the standard Skin-Color Model, and make the Skin-Color model adapt to different users' skin color which is to segment the skin color accurately. Then use the density distribution feature of hand to recognize hand gestures, and the shape features of gestures could be obtained by computing the disposition of pixel points in hand areas with different distance. Experimentations show that, the improved method could detection hand in complex background, no meter if the rotation angel and proportion are large or small. Several experimental images are presented to support the validity of the method.

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.