# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 5 Conclusions  
In this work, we present an evaluation of datasets for vision-based emotion recognition, focusing on datasets that allow more than one visible cue simultaneously. We propose experiments to explore the data and correlate it with findings from the psychology literature. From these experiments, among other insights and highlights, we show that the difference between the two datasets most applied in the state-of-the-art techniques is significant, due to the many different features employed in them. The CAER and CAER-S datasets provide a more stable benchmark, with images with faces centered in relation to the camera. In contrast, EMOTIC provides a benchmark that is more unpredictable. Both datasets employ an annotation format that can be prejudicial on some level, according to the planned application scenario. EMOTIC uses 26 categories to represent emotion, which does not include neutrality. In this case, the high number of possible categories could impact annotator agreement and harm some applications. While CAER uses a reduced set \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**EMOTIC and BoLD** & **CAER and AfeW** & **iMiGUE** & **GroupWalk** \\ \hline
1. **Peace**: well being and relaxed; no worry; having positive thoughts or sensations; satisfied & & & \\
2. **Affection**: fond feelings; love; tenderness & & & \\
3. **Esteem**: feelings of favorable opinion or judgment; respect; admiration; gratefulness & & & \\
4. **Anticipation**: state of looking forward; hoping for or getting prepared for possible future events & & & \\
5. **Engagement**: paying attention to something; absorbed into something; curious; interested & & & \\
6. **Confidence**: feeling of being certain; conviction that an outcome will be favorable; encouraged; proud & & & \\
7. **Heppiness**: feeling delighted; feeling enjoyment or amusement & & & \\
8. **Pleasure**: feeling of delight in the senses & & & \\
9. **Excitement**: feeling enthusiasm; stimulated; energetic & & & \\
10. **Surprise**: sudden discovery of something unexpected & & & \\
11. **Symptaty**: state of sharing others emotions, goals or troubles; supportive; compassionate & & & \\
12. **Doubt/Confusion**: difficulty to understand or decide; thinking about different options & & & \\
13. **Disconnection**: feeling not interested in the main event of the surroundings; indifferent; bored; distracted & & & \\
14. **Fatigue**: weariness; tiredness; sleepy & & & \\
15. **Embarrassment**: feeling ashamed or guilty & & & \\
16. **Yearning**: strong desire to have something; jealous; envious; lust & & & \\
17. **Disapproval**: feeling that something is wrong or reprehensible; contempt; hostile & & & \\
18. **Aversion**: feeling disgust, dislike, repulsion; feeling hate & & & \\
19. **Annoyance**: bothered by something or someone; irritated; impatient; frustrated & & & \\
20. **Anger**: intense displeasure or rage; furious; resentful & & & \\
21. **Sensitivity**: feeling of being physically or emotionally wounded; feeling delicate or vulnerable & & & \\
22. **Sadness**: feeling unhappy, sorrow, disappointed, or discouraged & & & \\
23. **Disquitement**: nervous; worried; upset; anxious; tense; pressured; alarmed & & & \\
24. **Fear**: feeling suspicious or afraid of danger, threat, evil or pain; horror & & \\
25. **Pain**: physical suffering & & & \\
26. **Surfering**: psychological or emotional pain; & & & \\ \end{tabular}
\end{table} 
Table 6: Emotion categories employed in the datasets reviewed in this work. of categories, comprised of [PERSON]'s six emotional categories and a neutrality label, they also lack descriptions regarding to whom that annotation was attached in the scene. We also evaluate datasets that are less common in the literature, such as BoLD, MiGUE, and GroupWalk, which are focused on specific cues and that could be very aligned with some application scenarios. Finally, we discuss these scenarios and suggest approaches based on findings from these datasets to help guide future research and deployment.