Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection
| Autor: | Krist Shingjergji, Deniz Iren, Felix Bottger, Corrie Urlings, Roland Klemke |
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| Přispěvatelé: | Department of Information Science, RS-Research Program Learning and Innovation in Resilient systems (LIRS), RS-Research Program Educational research on activating (online) education (ERA), Department of Technology Enhanced Learning and Innovation, RS-Research Line Technology Enhanced Learning and Innovation (part of ERA program) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
facial emotion recognition Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction Affective computing gamification interpretable machine learning Human-Computer Interaction (cs.HC) explainable AI |
| Zdroj: | Shingjergi, K, Iren, Y D, Klemke, R, Urlings, C C J & Böttger, F 2022, Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection . in 2022 10th International Conference on Affective Computing and Intelligent Interaction, ACII 2022 . IEEE, 10th International Conference on Affective Computing and Intelligent Interaction, Nara, Japan, 18/10/22 . https://doi.org/10.1109/ACII55700.2022.9953864 IEEE Affective Computing and Intelligent Interaction |
| DOI: | 10.1109/ACII55700.2022.9953864 |
| Popis: | Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portrays a particular basic emotion. Every round played by the player creates new data that consists of a set of facial features and landmarks, already annotated with the emotion label of the target facial expression. Such an approach effectively creates a robust, sustainable, and continuous machine learning training process. We evaluated Facegame with an experiment that revealed several contributions to the field of affective computing. First, the gamified data collection approach allowed us to access a rich variation of facial expressions of each basic emotion due to the natural variations in the players' facial expressions and their expressive abilities. We report improved accuracy when the collected data were used to enrich well-known in-the-wild facial emotion datasets and consecutively used for training facial emotion recognition models. Second, the natural language prescription method used by the Facegame constitutes a novel approach for interpretable explainability that can be applied to any facial emotion recognition model. Finally, we observed significant improvements in the facial emotion perception and expression skills of the players through repeated game play. Comment: 8 pages, 8 figures, 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII) |
| Databáze: | OpenAIRE |
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