Dynamic hand gesture recognition using motion trajectories and key frames

In this paper, we address the problem of dynamic hand gesture recognition from unaided video sequences. We present a novel approach based on motion trajectories of hands and hand shapes of the key frames. Firstly, the hand area is segmented by active skin color model. Then the hand motion trajectories are extracted with dynamic time warping (DTW) algorithm and the key frame of video sequences is computed by frames difference. The hand gesture of the key frame is considered as a static hand gesture. The feature of hand shape is represented with Fourier descriptor and recognized by neural network. The combined method of the motion trajectories and key frame is presented to recognize the dynamic hand gesture from unaided video sequences. Experimental results show that the proposed approach is capable of effectively recognizing the dynamic hand gesture.