# Learning Person-specific Network Representation for Apparent Personality Traits Recognition

**Authors:** Fang Li  
**arXiv:** [2303.1236](https://arxiv.org/abs/2303.1236)  
**Date:** 2023-03-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.1236v1/

## Key points

- Human facial behavior dynamics reflect personality traits
- Existing methods for personality recognition only encode short-term facial behaviors
- A new approach involves training a person-specific network for each subject to model multi-scale long-term person-specific behavior evolution
- The weights of the network contain person-specific facial behavior-related cues of the subject and are encoded into a graph representation for personality recognition using Graph Neural Networks (GNNs)
- Experimental results show that this approach outperforms most traditional latent feature-based approaches and has comparable performance to state-of-the-art methods
- The proposed method provides a more comprehensive understanding of how facial behaviors reflect apparent personality traits by modeling long-term behavioral evolution and incorporating person-specific cues into the analysis.

## Summary

The study of human facial behavior dynamics has shown that apparent personality traits can be reflected through them. To address the limitation of existing methods for personality recognition which only encode short-term facial behaviors in the latent features, a new approach is proposed in this paper for recognizing apparent personality traits. The approach involves training a person-specific network for each subject to model multi-scale long-term person-specific behavior evolution. It is hypothesized that the weights of the network contain person-specific facial behavior-related cues of the subject. These weights are then encoded into a graph representation which serves as the personality representation for the subject and can be processed by standard Graph Neural Networks (GNNs) for personality trait recognition. Experimental results show that this novel network weights-based approach outperforms most traditional latent feature-based approaches and has comparable performance to state-of-the-art methods. Additionally, it produces robust results when using different GNNs and further validates that person-specific networks' weights are correlated to the subject's personality. The proposed method provides a more comprehensive understanding of how facial behaviors reflect apparent personality traits by modeling long-term behavioral evolution and incorporating person-specific cues into the analysis. This approach has significant implications for improving accuracy in personality recognition tasks and could potentially lead to better insights into how human behavior relates to individual differences in personality traits.