Polar-Radius-Invariant-Moment Based on Key-Points for Hand Gesture Shape Recognition

For the whole matching cannot handle partial occlusion and lack of specificity, a new method using PolarRadius-Invariant-Moment, which is based on Key-Points to extract features for target’s shape recognition, is presented in this paper. Firstly, key-points of the hand shape are extracted through discrete curve evolution method. Secondly, Polar-Radius-Invariant-Moment based on KeyPoints is used to describe the characteristics of the gesture shape. Finally, Euclidean distance is utilized in gesture recognition to verify the validity of this method. Hand objects are selected as the test case to testify the performance of the method. Simulation results prove that this method has a better classification character than that of obtained by the Polar-Radius-Invariant-Moment with recognizing the object shape rapidly and accurately, and that it also can keep highly stable even if the object contour was ill-segmented or noisy.