# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 4 Discussion
### Application Scenarios
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset** & **Suggested Application Scenarios** & **Possible Limitations** \\ \hline EMOTIC [16; 53] & Images in which context is highly representative and/or highly variable. & \\ \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & Content published on social networks. & \\  & Scenes in which the face can be occluded. & \\  & Images sampled from cameras aligned with the ground. & \\  & Applications that require a deeper comprehension of sentiments instead of a direct emotion classification. & \\ \hline CAER/CAER-S [17] & Images or videos in which the face will mostly be aligned to the camera. & \\  & Interviews, one-on-one meetings, reactions, or other scenarios where the camera will be mounted at eye level. & \\  & Feedback for retailers on individual spaces or services. & \\  & Generic applications in which a direct classification is sufficient. & \\  & Affective computing on notebooks, smartphones, tablets, or other mobile devices with a front-facing camera. & \\ \hline iMiGUE [54] & Spontaneous scenarios in which subtle cues need to be considered. & \\  & Interviews, one-on-one meetings, reactions, or other scenarios where the camera will be mounted at eye level. & \\  & Applications with high reliance on body language and gestures. & \\ \hline AfeW/AfeW-VA [51; 52] & Scenes with low context contribution. & \\  & Affective computing on notebooks, smartphones, tablets, or other mobile devices with a front-facing camera. & \\  & Feedback for retailers on individual spaces or services. & \\  & Scenes in which subtle facial expressions do not need to be considered & \\ \hline \hline AfeW/AfeW-VA [51; 52] & Images or videos with representative body language. & \\  & Applications with a deeper emotion representation related to multiple cues simultaneously. & \\  & Social interactions. & \\  & Applications that are expected to work with people from diverse ethnicities. & \\  & Images sampled from cameras aligned with the ground. & \\  & Scenes in which the face can be occluded. & \\ \hline \hline \end{tabular} & * Low resolution in some samples could impact learning from facial expressions. * Lack of neutral classification may generate noise in some applications. * Lack of ethnic representation means that techniques could be biased in some regions \\ \hline GroupWalk [56] & Scenes with a different social interaction. & * Noise from the dataset could be prejudicial to the model. * Gait is not always representative of emotion in this viewpoint. * Poor presence of other nonverbal cues. * Generic emotion categories. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the suggested application scenarios of each dataset and their possible limitations. Another possibility is related to retail or private businesses. Decision makers could place cameras with a more frontal view of people and explore the facial cues, for example. Cameras placed on the cashier and on the store exit could extract information about the user's experience in their store. For this case, AfeW, CAER, and CAER-S could be employed, given that they will always have a good facial crop of the performers in the set and that the body will only sometimes be evident, and because the context would not vary much. In cases where context variability is expected, EMOTIC could be helpful, given the different types of semantics that can be extracted from it. Furthermore, training on this dataset could help towards a robust model in cases where faces will not be visible. **Affective and assistive robotics. ** Another topic that has gained interest in the literature is assistive robotics. Currently, we have tasks from competitions in which the robot should follow someone around and help them with some tasks. However, these could also be extended into a more affective sense, allowing the development of affective robots to help people who need special care. In [86], the authors proposed CuDDLer, a social robot that was designed as a companion for children.