Abstract: Human posture recognition is one of the important research topics in human-computer interaction. With the development of machine learning and neural networks, the research methods and results tend to be diversified, and the application value of gesture recognition is becoming more and more extensive. This paper constructs a convolutional neural network model, which has 11 layers. It convolves and pools five kinds of human poses in the sampled data set, and finally enters the fully connected layer for classification, thus completing the training and identification of the data set. The results show that compared with the machine learning method, the recognition performance of the model is more excellent, and the complex feature extraction mode design is eliminated, so that the network itself extracts features to identify and classify better.

Key words: human-computer interaction, pose recognition, CNN