# Domain-specific Learning of Multi-scale Facial Dynamics for Apparent Personality Traits Prediction

arXiv: https://arxiv.org/abs/2209.04148 · HTML: https://mlsanity.com/paper/2209.04148/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Temporal Pyramid Network for Action Recognition](https://mlsanity.com/paper/2004.03548/) (2020)
- [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)
- [Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features](https://mlsanity.com/paper/1610.10048/) (2016)
- [On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals](https://mlsanity.com/paper/1909.05568/) (2019)
- [Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation](https://mlsanity.com/paper/2111.15266/) (2021)
- [Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition](https://mlsanity.com/paper/2110.13570/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learning Person-specific Network Representation for Apparent Personality Traits Recognition](https://mlsanity.com/paper/2303.01236/) (2023)

## Abstract (arXiv)

Human personality decides various aspects of their daily life and working behaviors. Since personality traits are relatively stable over time and unique for each subject, previous approaches frequently infer personality from a single frame or short-term behaviors. Moreover, most of them failed to specifically extract person-specific and unique cues for personality recognition. In this paper, we propose a novel video-based automatic personality traits recognition approach which consists of: (1) a domain-specific facial behavior modelling module that extracts personality-related multi-scale short-term human facial behavior features; (2) a long-term behavior modelling module that summarizes all short-term features of a video as a long-term/video-level personality representation and (3) a multi-task personality traits prediction module that models underlying relationship among all traits and jointly predict them based on the video-level personality representation. We conducted the experiments on ChaLearn First Impression dataset, and our approach achieved comparable results to the state-of-the-art. Importantly, we show that all three proposed modules brought important benefits for personality recognition.