Smart City Gnosys
Smart city article details
| Title | A Survey On Datasets For Emotion Recognition From Vision: Limitations And In-The-Wild Applicability |
|---|---|
| ID_Doc | 5244 |
| Authors | Costa W.; Talavera E.; Oliveira R.; Figueiredo L.; Teixeira J.M.; Lima J.P.; Teichrieb V. |
| Year | 2023 |
| Published | Applied Sciences (Switzerland), 13, 9 |
| DOI | http://dx.doi.org/10.3390/app13095697 |
| Abstract | Emotion recognition is the task of identifying and understanding human emotions from data. In the field of computer vision, there is a growing interest due to the wide range of possible applications in smart cities, health, marketing, and surveillance, among others. To date, several datasets have been proposed to allow techniques to be trained, validated, and finally deployed to production. However, these techniques have several limitations related to the construction of these datasets. In this work, we survey the datasets currently employed in state-of-the-art emotion recognition, to list and discuss their applicability and limitations in real-world scenarios. We propose experiments on the data to extract essential insights related to the provided visual information in each dataset and discuss how they impact the training and validation of techniques. We also investigate the presence of nonverbal cues in the datasets and propose experiments regarding their representativeness, visibility, and data quality. Among other discussions, we show that EMOTIC has more diverse context representations than CAER, however, with conflicting annotations. Finally, we discuss application scenarios and how techniques to approach them could leverage these datasets, suggesting approaches based on findings from these datasets to help guide future research and deployment. With this work we expect to provide a roadmap for upcoming research and experimentation in emotion recognition under real-world conditions. © 2023 by the authors. |
| Author Keywords | affective computing; computer vision; emotion perception; emotion recognition; emotion recognition datasets; human behavior recognition; in-the-wild emotion recognition; multi-cue emotion recognition; nonverbal emotion communication; survey |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
25996  | 0.87 | Farman H.; Sedik A.; Nasralla M.M.; Esmail M.A. | Facial Emotion Recognition In Smart Education Systems: A Review | Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023 (2023) |
| 27190 | 0.866 | Nguyen M.; Yan W.Q. | From Faces To Traffic Lights: A Multi-Scale Approach For Emotional State Representation | Proceedings - 2023 IEEE International Conference on High Performance Computing and Communications, Data Science and Systems, Smart City and Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2023 (2023) |
| 21000 | 0.865 | Sharma A.; Kumar A. | Dream: Deep Learning-Based Recognition Of Emotions From Multiple Affective Modalities Using Consumer-Grade Body Sensors And Video Cameras | IEEE Transactions on Consumer Electronics, 70, 1 (2024) |
| 9267 | 0.858 | Jha S.; Shah S.; Ghamsani R.; Sanghavi P.; Shekokar N.M. | Analysis Of Rnns And Different Ml And Dl Classifiers On Speech-Based Emotion Recognition System Using Linear And Nonlinear Features | Recurrent Neural Networks: Concepts and Applications (2022) |
| 23901 | 0.854 | Valen-Dacanay J.G. | Enhancing Proactive Immediate Response With Facial Emotion Detection Using Efficient Deep Learning | Lecture Notes in Networks and Systems, 1083 LNNS (2024) |
| 25997 | 0.852 | Alsharekh M.F. | Facial Emotion Recognition In Verbal Communication Based On Deep Learning | Sensors, 22, 16 (2022) |