An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

With the continuous improvement of people’s requirements for interactive experience, gesture recognition is widely used as a basic human-computer interaction. However, due to the environment, light source, cover, and other factors, the diversity and complexity of gestures have a great impact on gesture recognition. In order to enhance the features of gesture recognition, firstly, the hand skin color is filtered through YCbCr color space to separate the gesture region to be recognized, and the Gaussian filter is used to process the noise of gesture edge; secondly, the morphological gray open operation is used to process the gesture data, the watershed algorithm based on marker is used to segment the gesture contour, and the eight-connected filling algorithm is used to enhance the gesture features; finally, the convolution neural network is used to recognize the gesture data set with fast convergence speed. The experimental results show that the proposed method can recognize all kinds of gestures quickly and accurately with an average recognition success rate of 96.46% and does not significantly increase the recognition time.