Emotion is an important driver of human decision-making and communication. With the recent rise of human–computer interaction, affective computing has become a trending research topic, aiming to develop computational systems that can understand human emotions and respond to them. A systematic review has been conducted to fill these gaps since previous reviews regarding machine-enabled automated visual emotion recognition neglect important methodological aspects, including emotion models and hardware usage. 467 relevant papers were initially found and examined. After the screening process with specific inclusion and exclusion criteria, 30 papers were selected. Methodological aspects including emotion models, devices, architectures, and classification techniques employed by the selected studies were analyzed, and the most popular techniques and current trends in visual emotion recognition were identified. This review not only offers a comprehensive and up-to-date overview of the topic but also provides researchers with insights regarding methodological aspects like emotion models employed, devices used, and classification techniques for automated visual emotion recognition. By identifying current trends, like the increased use of deep learning algorithms and the need for further study on body gestures, this review advocates the advantages of implementing emotion recognition with the use of visual data and builds a solid foundation for applying relevant techniques in different fields.

情绪是人类决策和沟通的重要驱动力。随着最近人机交互的兴起，情感计算已成为一个热门的研究课题，旨在开发能够理解人类情感并对其做出反应的计算系统。由于之前关于机器启用的自动视觉情感识别的评论忽略了重要的方法方面，包括情感模型和硬件使用，因此已经进行了系统的评论以填补这些空白。初步发现并检查了 467 篇相关论文。经过具有特定纳入和排除标准的筛选过程后，选择了 30 篇论文。分析了所选研究采用的方法论方面，包括情绪模型、设备、架构和分类技术，并确定了视觉情绪识别中最流行的技术和当前趋势。这篇综述不仅提供了对该主题的全面和最新的概述，而且还为研究人员提供了有关方法论方面的见解，例如采用的情感模型、使用的设备以及自动视觉情感识别的分类技术。通过识别当前趋势，如深度学习算法的使用增加和对身体姿势的进一步研究的需要，这篇综述提倡使用视觉数据实现情绪识别的优势，并为在不同领域应用相关技术奠定坚实的基础. 这篇综述不仅提供了对该主题的全面和最新的概述，而且还为研究人员提供了有关方法论方面的见解，例如采用的情感模型、使用的设备以及自动视觉情感识别的分类技术。通过识别当前趋势，如深度学习算法的使用增加和对身体姿势的进一步研究的需要，这篇综述提倡使用视觉数据实现情绪识别的优势，并为在不同领域应用相关技术奠定坚实的基础. 这篇综述不仅提供了对该主题的全面和最新的概述，而且还为研究人员提供了有关方法论方面的见解，例如采用的情感模型、使用的设备以及自动视觉情感识别的分类技术。通过识别当前趋势，如深度学习算法的使用增加和对身体姿势的进一步研究的需要，这篇综述提倡使用视觉数据实现情绪识别的优势，并为在不同领域应用相关技术奠定坚实的基础.