Abstract: Gesture recognition is a very important technology in human-computer interaction,which has high theoretical and practical value.However,due to the complexity of the background and individual differences,gesture recognition has become a challenging topic.Therefore,it is necessary to design an efficient and accurate gesture recognition algorithm to effectively recognize the detected target gesture.An improved method of gesture segmentation and gesture feature extraction is proposed.SVM classifier is used to construct gesture model and recognize gesture.On the basis of YCrCb color space,OTSU threshold processing method is combined to select threshold segmentation gesture to improve the accuracy of segmentation.On the basis of edge detection,the ellipse Fourier descriptor is used to fit the edge and extract gesture features.Experimental results show that the system based on the above algorithm can extract gesture feature information efficiently,and the average recognition accuracy of 13 common gestures in a simple background is 89.96%,which can basically meet the requirements of recognition accuracy and stability.

Key words: OpenCV, Skin color detection, Gesture segmentation, Feature extraction, SVM classifier