Macro-and Micro-Expressions Facial Datasets: A Survey

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

Interpersonal relation recognition: a survey

People spend a considerable amount of their time in social activities, where person-to-person relations are of main relevance. Recently, there has been an increasing research interest in automatically analyzing interpersonal relations, for the social and behavioral implications, and the many practical applications it may have. However, to the best of our knowledge, there is not a systematic study providing a harmonized view of the literature in the field. On this ground, we summarize in our work interpersonal relation recognition datasets and methods aiming to help researchers to have a better understanding of the characteristics of the state-of-the-art. In the proposed study, we distinguish between methods that address objective relations that do not depend on behavior or emotional state, and methods that consider subjective ones that depend on emotions. It turns out quite evidently that aiming at the latter recognition task is more challenging, with the existing methods that provide convincing results only on limited and very specific cases. For both the broad categories, we discuss datasets and methods according to the different behavioural and psychological models used to annotate and classify the data. We conclude our review work, by providing a comprehensive discussion pointing out current limitations and future research perspectives