Research on Hand Gesture Recognition Based on Inner-Distance Contour Point Distribution Features and Histogram Matching

Aiming at the influence of joints or part structures deformations on the accuracy of gesture recognition in representing, and large amount of calculation with shape matching directly, a method based on inner-distance contour point distribution features (IDCPDF) and histogram matching is proposed in this paper. Firstly, elliptical skin model is used to segment and extract contour. Then IDCPDF of gestures is generated. Finally, histogram matching is used to measure the similarity of IDCPDF and classify. Experimental results show that the method describes distributions of gesture contour points under polar coordinates. It not only reflects significant information of gesture shapes, but also reduces calculations in gesture features extraction and matching on the promise of ensuring gesture recognition accuracy, and achieves better real-time performance. Meanwhile, this method keeps good robustness on joints and part structures deformations of hands.