Abstract: In order to build a database of classroom expression images and make up for the lack of classroom expression diversity under specific conditions， a method for generating classroom expression images based on deep convolutional generative adversarial networks （DCGAN） is proposed. Firstly， by using the offline teaching surveillance videos and the online classroom videos to independently collect classroom expression images， and a balanced and small image set with abundant sample features is obtained. Secondly， the training image set of classroom expression is constructed by image denoising， image enhancing and image mirroring. Thirdly， through the construction and preliminary parameter setting of the classroom expression image generation network based on DCGAN model， and constantly optimizing the network hyperparameters， the classroom expression image dataset is generated. Finally， the face detection algorithm and the IS （Inception Score） evaluation index are used to detect and evaluate the generated classroom expression images， and verify the feasibility and effectiveness of the generated images in the detection network. The experimental results show that the method based on DCGAN can generate more realistic classroom expression images， effectively improve the classroom facial expression dataset， and enhance the diversity of classroom expression images.

Key words: deep learning, DCGAN （deep convolutional generation adversarial networks）, image generation, classroom expression