A Gesture Recognition Method Based on Deep Learning - DING Chi; LIN Jun; YOU Jun; YUAN Hao (Kongzhi Yu Xinxi Jishu)
In order to increase the accuracy and robustness of gesture recognition in video, and better utilize the advantages of different deep neural network architectures, it presented an approach to realize gesture recognition by combining multiple advanced convolutional neural network architectures. This method implements multi-scale human hand detection with a depth convolutional neural network based on the SSD architecture. Through matching and screening of multiple predictive boxes with different sizes ground truth boxes, it acquired the position of all hands in the input image and return the position coordinates. Then, another convolutional neural network based on Multiview Bootstrapping method was used to detect the hand key points from 2D images in complex environment through multiple perspectives. Finally, several common gestures were defined and a gesture classifier was established to classify the gestures based on the hand key point features. The experimental result shows that the proposed method is robust and accurate for different types of gestures in complex environment.