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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.00205 |
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| _version_ | 1866915421307797504 |
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| author | Kong, Xiangyu Zhu, Hengde Sun, Haoqin Guo, Zhihao Gu, Jiayan Ni, Xinyi Zhang, Wei Liu, Shizhe Song, Siyang |
| author_facet | Kong, Xiangyu Zhu, Hengde Sun, Haoqin Guo, Zhihao Gu, Jiayan Ni, Xinyi Zhang, Wei Liu, Shizhe Song, Siyang |
| contents | Automatic real personality recognition (RPR) aims to evaluate human real personality traits from their expressive behaviours. However, most existing solutions generally act as external observers to infer observers' personality impressions based on target individuals' expressive behaviours, which significantly deviate from their real personalities and consistently lead to inferior recognition performance. Inspired by the association between real personality and human internal cognition underlying the generation of expressive behaviours, we propose a novel RPR approach that efficiently simulates personalised internal cognition from easy-accessible external short audio-visual behaviours expressed by the target individual. The simulated personalised cognition, represented as a set of network weights that enforce the personalised network to reproduce the individual-specific facial reactions, is further encoded as a novel graph containing two-dimensional node and edge feature matrices, with a novel 2D Graph Neural Network (2D-GNN) proposed for inferring real personality traits from it. To simulate real personality-related cognition, an end-to-end strategy is designed to jointly train our cognition simulation, 2D graph construction, and personality recognition modules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition Kong, Xiangyu Zhu, Hengde Sun, Haoqin Guo, Zhihao Gu, Jiayan Ni, Xinyi Zhang, Wei Liu, Shizhe Song, Siyang Computer Vision and Pattern Recognition Automatic real personality recognition (RPR) aims to evaluate human real personality traits from their expressive behaviours. However, most existing solutions generally act as external observers to infer observers' personality impressions based on target individuals' expressive behaviours, which significantly deviate from their real personalities and consistently lead to inferior recognition performance. Inspired by the association between real personality and human internal cognition underlying the generation of expressive behaviours, we propose a novel RPR approach that efficiently simulates personalised internal cognition from easy-accessible external short audio-visual behaviours expressed by the target individual. The simulated personalised cognition, represented as a set of network weights that enforce the personalised network to reproduce the individual-specific facial reactions, is further encoded as a novel graph containing two-dimensional node and edge feature matrices, with a novel 2D Graph Neural Network (2D-GNN) proposed for inferring real personality traits from it. To simulate real personality-related cognition, an end-to-end strategy is designed to jointly train our cognition simulation, 2D graph construction, and personality recognition modules. |
| title | Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.00205 |