ABSTRACT
This study aims to investigate the relationship between facial expressions and personality description and verify whether there is a difference between human-machine and human-human interactions. The data provided by Osaka University, including facial expressions and some questionnaires, are helpful. As a result, the relationships between facial expressions and personality descriptions are observed in cases of disgust and fear. Measurements related to the conversationalist's perception of interpersonal communication influence facial expressions such as disgust and fear. This result implies that participants' emotions when interacting with the machine are possibly expressed in such negative cases. Also, it was found that lots of personality traits widely affect happiness, sadness, and surprise.
- Ahlberg, J., Forchheimer, R. (2003). Face tracking for model-based coding and face animation. Int. J. Imaging Syst. Technol. 13(1), 8–22Google ScholarCross Ref
- Bartlett, M., Hager, J., Ekman, P., Sejnowski, T. (1999). Measuring facial expressions by computer image analysis. Psychophysiology 36, 253–264Google ScholarCross Ref
- Caifeng Shan, Shaogang Gong, Peter W. McOwan. (2009). Facial expression recognition based on Local Binary Patterns. A comprehensive study, Image and Vision Computing. Volume 27, Issue 6., 803-816, https://doi.org/10.1016/j.imavis.2008.08.005.Google ScholarDigital Library
- Komatani Kazunori and Shogo Okada (2021). Multimodal Human-Agent Dialogue Corpus with Annotations at Utterance and Dialogue Levels. 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Nara, Japan, 2021,1-8, doi: 10.1109/ACII52823.2021.9597447.Google ScholarCross Ref
- Komatani Kazunori and Shogo Okada (2022). Osaka University Multimodal Dialogue Corpus (Hazumi). Informatics Research Data Repository, National Institute of Informatics. (dataset). https://doi.org/10.32130/rdata.4.1Google ScholarCross Ref
- Oshio Atsushi , Shingo Abe, and Pino Cutrone.(2012). Development, Reliability, and Validity of the Japanese Version ofGoogle Scholar
- Ten Item Personality Inventory (TIPI-J). The Japanese Journal of Personality 2012, Vol. 21 No. 1, 40–520Google Scholar
- Shan Li and W. Deng. (2022). Deep Facial Expression Recognition. A Survey. IEEE Transactions on Affective Computing. vol. 13, no. 3, 1195-1215, 1 July-Sept. 2022, doi: 10.1109/TAFFC.2020.2981446.Google ScholarCross Ref
- Tian, Y., Kanade, T., Cohn, J.F. (2011). Facial Expression Recognition. In: Li, S., Jain, A. (eds) Handbook of Face Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-932-1_19Google ScholarCross Ref
Index Terms
- Facial Expression and Description of Personality
Recommendations
Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos
This paper proposes a facial expression recognition (FER) method in videos. The proposed method automatically selects the peak expression face from a video sequence using closeness of the face to the neutral expression. The severely non-frontal faces ...
Recognizing action units for facial expression analysis
Multimodal interface for human-machine communicationMost automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more ...
Facial Landmark-Based Human Emotion Recognition Technique for Oriented Viewpoints in the Presence of Facial Attributes
AbstractWith the expansion of machine learning and deep learning technology, facial expression recognition methods have become more accurate and precise. However, in a real-case scenario, the presence of facial attributes, weakly posed expressions and ...
Comments