# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 3 Datasets used in State-of-the-Art Techniques
The psychology literature tackles the problem of posed emotions, and evidence suggests that they are at least an approximation of what is actually felt. Studies show that we can diminish the impact of these problems by using different actors in the experiments, that are unaware of the task--in this case, the actors were unaware that this footage would be used for the emotion recognition task. Therefore, we can consider that the negative impact is low [4; 32; 59]. The Body Language Dataset (BoLD) [55] has a different focus from EMOTIC and CAER. While also containing contextual information from background scenes, its focus is on the availability and description of body language, another significant nonverbal cue that the current literature has yet to adopt [19; 20]. The authors' proposal concerns a dataset with crowdsourced emotional information from videos that were validated using experiments with action recognition-based methods and Laban movement analysis features. The construction process of the dataset is based on three stages related to selection (of clips and time period), annotation of pose (pose estimation and tracking), and perceived emotional features. Given the intersection of these cues for activity recognition, the authors choose clips from the AVA dataset [60] for annotation. Each person receives a unique ID number, which is tracked among frames, and has their emotions annotated. Focusing on the visibility of the body, close-up clips are removed. The dataset contains 26,164 video clips with 48,037 instances, defined as an identifiable character with landmark tracking in a clip. Each instance is annotated by 5 participants, who annotate 20 samples. This dataset is widely representative with varied demographics, especially ethnicity categories that include sub-represented groups such as _Hispanic or Latino, Native Hawaiian or Other Pacific Islander_, for example. Although the dataset is promising, it is still not widely used in the literature on emotion recognition, even though it could lead to interesting in-the-wild applications. The GroupWalk dataset [56] comprises videos recorded with stationary cameras in eight real-world scenarios. A total of 10 annotators annotated 3544 agents with visible faces across all videos. This dataset is still to be widely used in the current literature given its focus on a very specific nonverbal cue. The Micro-Gesture Understanding and Emotion analysis (iMiGUE) dataset [54] focuses on nonverbal body gestures, specifically microgestures, to understand perceived emotion. The dataset contains samples extracted from post-match press conferences with professional athletes, which contain footage from interviews and questions after sports matches. In these cases, the player has little to no time to prepare for the questions. As a result, the expressions and reactions are usually not posed and have insightful reflections on the innate emotion. The authors collected 359 videos with 258 wins and 101 losses from post-match grand slam tournament interviews, totaling 2092 min in duration. The dataset is identity-free, meaning that biometric data, such as the face and voice, have been masked. They also focus on ethnic diversity and gender balance. Five annotators were responsible for annotating the data, which contained information from body, head, hand, body+hand, and head+hand.