Macro- and Micro-Expressions Facial Datasets: A Survey
Hajer Guerdelli,
Claudio Ferrari,
Walid Barhoumi,
Haythem Ghazouani,
Stefano Berretti
Affiliations
Hajer Guerdelli
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
Claudio Ferrari
Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy
Walid Barhoumi
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
Haythem Ghazouani
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
Stefano Berretti
Media Integration and Communication Center, University of Florence, 50121 Firenze, Italy
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.
