Abstract
Personality and emotion as intrinsic factors often have great influences on the cognition of people’s behavior. In computer vision, there is a lot of work done on the recognition of emotions, such as classification of a person’s emotions via analyzing facial expressions. Relatively there is less work done on personality estimation. Personality, as a long-term characteristic pattern of behavior, influences the emotion generation of a person. In this paper, we present a new method to analyze and estimate personality and emotions in dyadic and multiparty social interactions. We first propose a context-aware deep learning framework that automatically estimates the personality of a target person based on his/her own and the interlocutor’s body behavioral and facial information recorded in the interaction process. Then, we expand this architecture to form a method for jointly estimating personality and recognizing emotions. We conduct a series of experiments on two datasets and the experimental results show that the proposed method has good performance in both personality estimation and emotion recognition.
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The two datasets “MHHRI” and “MUMBAI” used in this study are available from Celiktutan et al. [6] and Doyran et al. [8], respectively, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data may, however be available from the authors upon reasonable request.
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Author Professor Jianmin Zheng is an Editorial Board Member of the Visual Computer and author Professor Nadia Magnenat Thalmann is the Editor-in-Chief of the Visual Computer.
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Zhang, Z., Zheng, J. & Thalmann, N.M. Context-aware personality estimation and emotion recognition in social interaction. Vis Comput 40, 5123–5137 (2024). https://doi.org/10.1007/s00371-023-02862-6
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DOI: https://doi.org/10.1007/s00371-023-02862-6