Abstract
Previous research has revealed differences in personality traits among different genders, age groups, and even cultures. However, existing methods have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles. In this paper, we propose to learn personalised models of self-reported big five personality traits. Our proposed approach automatically learns deep learning architectures for different user profiles using Neural Architecture Search (NAS) for predicting the Big Five personality traits from multimodal behavioural features. We experiment with two different user profiling criteria, namely, gender and age, and compare the results of our approach with the state-of-the-art methods. Overall, our results show that personalised models improve the performance as compared to the generic model. Particularly, gender-based user profiling combined with bimodal features reduces the prediction error by 0.128, achieving the state-of-the-art performance on the UDIVA dataset.
Original language | English (US) |
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Pages (from-to) | 53-73 |
Number of pages | 21 |
Journal | Proceedings of Machine Learning Research |
Volume | 173 |
State | Published - 2021 |
Event | ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on Computer Vision, ICCV 2021 - Virtual, Online Duration: Oct 16 2021 → … |
Keywords
- Automatic personality recognition
- multimodal human behaviour analysis
- Neural Architecture Search
- personalised models
- personality computing
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability