Abstract:More natural and flexible gesture recognition technology is gradually becoming an important human-machine interface for intelligent mobile robot control. In order to further improve the real-time and accuracy of robot vision control based on computer vision, a gesture recognition method based on Principal Component Analysis (PCA) dimension reduction combined with machine learning algorithm is proposed. First, the gesture image captured by the vision camera is preprocessed, including image binarization, median filtering, and morphological transformation. Then the background subtraction is used for feature extraction, and then the main features will be extracted by PCA and the data is reduced. Finally, combined with the more advanced self-organizing neural network in machine learning as a classifier, it is applied to gesture recognition. The static gesture experiment results show that compared with BP neural network and K-means algorithm, the proposed method could shorten the gesture recognition time and the recognition accuracy will be effectively improved. Therefore, the effectiveness of the proposed method has been verified.