目的 为解决真实环境中由类内差距引起的面部表情识别率低及室内外复杂环境对类内差距较大的面部表情识别难度大等问题，提出一种利用生成对抗网络（generative adversarial network，GAN）识别面部表情的方法。方法 在GAN生成对抗的思想下，构建一种IC-GAN（intra-class gap GAN）网络结构，使用卷积组建编码器、解码器对自制混合表情图像进行更深层次的特征提取，使用基于动量的Adam（adaptive moment estimation）优化算法进行网络权重更新，重点针对真实环境面部表情识别过程中的类内差距较大的表情进行识别，使其更好地适应类内差异较大的任务。结果 基于Pytorch环境，在自制的面部表情数据集上进行训练，在面部表情验证集上进行测试，并与深度置信网络（deep belief network，DBN）和GoogLeNet网络进行对比实验，最终IC-GAN网络的识别结果比DBN网络和GoogLeNet网络分别提高11%和8.3%。结论 实验验证了IC-GAN在类内差距较大的面部表情识别中的精度，降低了面部表情在类内差距较大情况下的误识率，提高了系统鲁棒性，为面部表情的生成工作打下了坚实的基础。

Objective China has a large population of 1.3 billion in 2005, accounting for 19% of the world's population. This number is equivalent to the population of Europe or Africa added to the populations of Australia, North America, and Central America. It is one of the few populous countries in the world, and its huge population size has brought many problems. With the rapid development of the economy, the number of people working outside of their homes is increasing, the population is moving frequently, and the safety of the floating population is even difficult to control. The huge mobile population provides the city's infrastructure and public services tremendous pressure. Thus, to conduct a comprehensive check on the area of adult traffic, which is time-consuming and labor-intensive, is difficult for security and related staff. Particularly, complex environmental safety problems, such as subways, railway stations, and airports, are becoming increasingly serious. Unstable events occur frequently, security situation is receiving much attention, and urban management and service systems are seriously lagging behind. These conditions need to be improved, especially after the September 11 incident in the United States. The situation has aroused widespread concern in the international community. Meanwhile, expression is the most intuitive way for humans to express emotions. In addition to language communication, expressions are extremely effective means of communication. People usually express their inner feelings through specific expressions. Expression can be used to judge other person's thoughts. For expressions used to express information, psychologist Mehrabian summed up a formula:emotional expression=7% of words + 38% of sound + 55% of facial expressions. Expression is one of the most important features of human emotion recognition. Expression is the emotional state expressed by facial muscle changes. Through the facial expression of the person's face, to evaluate abnormal psychological state, speculate on extreme emotions, and observe the facial expressions of pedestrians in the subway, railway station, and airport to further judge the psychology of the person is possible. We provide technical support to determine who is suspicious and prevent certain criminal activities in a timely manner. Strengthening urban surveillance and identifying the facial expressions of criminals are especially important. Expression plays an important role in human emotion cognition. However, factors affecting facial expression recognition in safety screening are extremely large, and the large intra-class gap seriously inflluences the accuracy of facial expression recognition. The problem of large gaps in facial expression recognition in a real environment is solved by identifying suspected molecules to be monitored should be identified, and the security personnel should prepare in advance to accurately identify them. Facial expressions are also particularly important for preventing security problems. The era of large data has arrived. Meanwhile, with the advancement of computer hardware, deep learning continues to develop. The traditional facial expression recognition method cannot meet the needs of the development of the times, and a new algorithm based on deep learning facial expression recognition is coming soon. Learning methods are widely used in facial expression recognition. Although facial recognition intelligent recognition technology has a long history of research, a large number of research methods have been proposed. However, due to the large facial expression gap, the expression is complex, and the influencing factors are many. The current intelligent recognition effect of facial expression results is not ideal. Considering the deep learning because of its powerful expressive ability, this study introduces the model structure of traditional neural network and carries out corresponding experiments and analysis in the context of real-life facial expression recognition and proposes real-world facial expression recognition research based on deep learning. In the next period, real-world facial expression recognition will make considerable progress. This work further studies the realistic facial expression recognition based on deep learning. Method This study constructs a new IC-GAN(intra-class gap GAN(generative adversarial network)) recognition network model, providing good adaptability to the facial expression recognition task with large gap within the class. The network consists of a convolutional layer, a fully connected layer, an active layer, a BathNorm layer, and a Softmax layer, in which a convolutional assembly encoder and a decoder are used to perform deep feature extraction on facial expression images, and download and parse from the network. The video self-made mixed facial expression data set is based on the real environment, the image is expanded, and the facial expression data are normalized. The complexity of the facial expression features with large differences within the class also increased the network training and network recognition. The momentum-based Adam is used to update the network weight, adjust the network parameters, and optimize the network structure based on this factor. In this study, the facial expression category data are trained based on the Pytorch platform in deep learning and tested on the verification set of the self-made mixed facial expression data set. Result When the input image is 256×256 pixels, the IC-GAN network model can reduce the false positive rate of the expression in large difference in the class, image blur, and facial expression incompleteness, and improve the system robustness. Compared with deep belief network(DBN) deep trust network and GoogLeNet network, the recognition result of IC-GAN network is 11% higher than that of DBN network and 8.3% higher than that of GoogLeNet network. Conclusion The IC-GAN accuracy in facial expression recognition with large gaps in the class is verified by experiments. This condition reduces the misunderstanding rate of facial expressions in large intra-class differences, improves the system robustness, and lays down the solid foundation for facial expression generation.