Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de'Information et dea Connaissance (LIMTIC), Institut Supérieur d'Informatique d'El Manar, Université de Tunis El Manar, Tunis 1068, Tunisia.
Media Integration and Communication Center, University of Florence, 50121 Firenze, Italy.
Sensors (Basel). 2022 Feb 16;22(4):1524. doi: 10.3390/s22041524.
Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro- and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application.
自动面部表情识别在许多潜在应用中至关重要。因此，在设计和评估有效的解决方案时，对已经在面部表情识别框架内进行研究的现有数据集有一个清晰的概述是至关重要的，特别是对于基于神经网络的训练。在这项调查中，我们回顾了 80 多个面部表情数据集，同时考虑了宏观和微观表情。由于研究的一个常见趋势是考虑在自然的环境中以自然的方式展示表情的背景，因此我们主要关注自发和野外数据集。我们还提供了所研究数据集的潜在应用实例，同时突出了它们的优缺点。该调查可以帮助研究人员更好地了解现有数据集的特点，从而方便选择最适合其应用特定背景的数据。
