Hand Gesture Recognition in Complex Background Based on improved Deep Residual Learning network

Although deep learning-based hand gesture recognition techniques have achieved amazing performance in recent years, it is challenges as hand gesture is prone to interference, such as light changes and gesture shadows on gesture recognition. In this paper, we propose a recognition model based on ellipse skin color, and improved Deep Residual Learning network for gesture recognition. By utilizing the clustering effect of skin color in YCbCr color space, we segment CbCr two-dimensional space based on the elliptical skin color model to hand gesture. To further improve performance, the mathematical morphology with logic operation is proposed to assist hand gesture segmentation. Lastly, the Deep Residual Learning architecture is used to increase the width of the network and the channel attention mechanism. The experimental results show that the proposed method can obtain a high recognition rate compared with existing methods.