轻量级网络在人脸表情识别上的新进展

作为人工智能领域的热门研究方向，人脸表情识别（facial expression recognition，FER）是让计算机获取人类感情最直接最有效的方式，在人机交互、智慧医疗、疲劳驾驶等研发课题中占据关键的技术地位。为了满足高识别率的应用需求，FER深度学习网络结构愈发复杂，占用了大量的计算资源和存储空间，严重影响了算法实时性的要求。围绕如何在有效提升模型运算速度的同时，保障模型的精度这一问题展开综述。首先，介绍了利用轻量级网络实现表情识别的重要数据集；其次，对用于人脸表情识别的经典轻量级网络模型进行了分析；再次，阐述了主要的网络轻量化方法的原理、特点及适用场景；最后，总结了轻量级网络在人脸表情识别研究中存在的问题和挑战，对未来的研究方向进行展望。

New advances in lightweight networks for facial expression recognition

As a popular research direction in the field of artificial intelligence, FER is the most direct and effective way for computers to access human emotions. It occupies a key technical position in human-computer interaction, intelligent medical care, fatigue driving, and other R&D topics. In order to meet the application requirements of high recognition rate, the structure of FER deep learning network becomes more and more complex, occupying a large amount of computing resources and storage space, which seriously affects the real-time requirements of the algorithm. This paper focused on the problem of how to guarantee the accuracy of the model while effectively improving its computational speed. Firstly, it introduced the important datasets for expression recognition using lightweight networks. Secondly, it analyzed the classical lightweight network models used for facial expression recognition. Thirdly, it described the principles, characteristics, and applicable scenarios of the main network lightweighting methods. Finally, it summarized the problems and challenges of lightweight networks in facial expression recognition research and looked forward to the future research direction.