Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania.
The National Institute for Research & Development in Informatics-ICI Bucharest, 011455 Bucharest, Romania.
Sensors (Basel). 2023 Aug 10;23(16):7092. doi: 10.3390/s23167092.
Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper's scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.
面部表情识别（FER）是一种计算机视觉过程，旨在检测和分类人类的情感表达。FER 系统目前在教育、医疗保健或公共安全等领域的各种应用中得到广泛应用；因此，检测和识别的准确性非常重要。与基于图像分析的任何计算机视觉任务类似，FER 解决方案也适合与人工智能解决方案集成，这些人工智能解决方案由不同的神经网络品种代表，特别是深度神经网络，由于其特征提取能力和在大型数据集上的计算效率，近年来在这方面显示出了巨大的潜力。在这种情况下，本文回顾了 FER 领域的最新发展，重点介绍了最近的神经网络模型，这些模型实现了特定的面部图像分析算法，以检测和识别面部表情。本文的范围是从历史和概念的角度介绍在 FER 领域取得显著成果的神经网络架构的演变。本文支持基于卷积神经网络（CNN）的架构，而不是其他神经网络架构，如递归神经网络或生成对抗网络，突出了每个架构的关键要素和性能，以及在分析论文中提出的模型的优点和局限性。此外，本文还介绍了目前用于从面部表情和微表情中识别情感的可用数据集。FER 系统在医疗保健、教育、安全或社交物联网等各个领域的应用也得到了强调。最后，确定了 FER 领域的未解决问题和未来可能的发展方向。
