Research on Gesture Recognition Based on Improved Canny & K-Means Algorithm and CNN

Abstract

In view of the problem that the current dynamic gesture recognition system has decreased recognition rate and recognition speed due to the increase of sample size,in this study, we first use the improved Canny operator to detect the edge of the hand image. The improved algorithm includes two aspects: one is to generate HSV space image converted from RGB space and extract the V-component image; the other is to transform the traditional Gauss filter into a bilateral filter which integrates spatial distance and similarity to denoise;Then the improved K-means clustering algorithm is used to extract the feature points on the edge of the hand image. In the improved method, the pixel points at the peak of histogram are used as the initial clustering center, and the number of peak points is used as the number of classification;Then, the detection algorithm of convex hull and convex defect with geometric features is used to realize the effective fingertip tracking;Finally, CNN is used to achieve the precise sorting of fingertips.After verification, this synthesis algorithm can effectively solve the problems such as the difficulty to accurately detect the hand image contour, the poor fingertip tracking effect and the disorder of fingertip sorting caused by the similarity of background color and skin color, and the small difference between the length of fingers. With the increase of sample size, the recognition rate and speed have also improved, which can be widely used in dynamic gesture recognition and other fields.