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

arXiv: https://arxiv.org/abs/2303.01236 · HTML: https://mlsanity.com/paper/2303.01236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition](https://mlsanity.com/paper/1609.05119/) (2016)
- [PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship](https://mlsanity.com/paper/1811.08657/) (2018)
- [An Open-source Benchmark of Deep Learning Models for Audio-visual Apparent and Self-reported Personality Recognition](https://mlsanity.com/paper/2210.09138/) (2022)
- [GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features](https://mlsanity.com/paper/2211.12482/) (2022)
- [Domain-specific Learning of Multi-scale Facial Dynamics for Apparent Personality Traits Prediction](https://mlsanity.com/paper/2209.04148/) (2022)

## Abstract (arXiv)

Recent studies show that apparent personality traits can be reflected from human facial behavior dynamics. However, most existing methods can only encode single-scale short-term facial behaviors in the latent features for personality recognition. In this paper, we propose to recognize apparent personality recognition approach which first trains a person-specific network for each subject, modelling multi-scale long-term person-specific behavior evolution of the subject. Consequently, we hypothesize that the weights of the network contain the person-specific facial behavior-related cues of the subject. Then, we propose to encode the weights of the person-specific network to a graph representation, as the personality representation for the subject, allowing them to be processed by standard Graph Neural Networks (GNNs) for personality traits recognition. The experimental results show that our novel network weights-based approach achieved superior performance than most traditional latent feature-based approaches, and has comparable performance to the state-of-the-art method. Importantly, the produced graph representations produce robust results when using different GNNs. This paper further validated that person-specific network's weights are correlated to the subject's personality.