# Pose as a Modality: A Psychology-Inspired Network for Personality Recognition with a New Multimodal Dataset

arXiv: https://arxiv.org/abs/2503.12912 · HTML: https://mlsanity.com/paper/2503.12912/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding](https://mlsanity.com/paper/2403.09626/) (2024)
- [FunASR: A Fundamental End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2305.11013/) (2023)
- [Low-Rank Bottleneck in Multi-head Attention Models](https://mlsanity.com/paper/2002.07028/) (2020)
- [FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba](https://mlsanity.com/paper/2404.09498/) (2024)
- [Preconditioned Stochastic Gradient Descent](https://mlsanity.com/paper/1512.04202/) (2015)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [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)

## Built on by (most cited first, 4 of 4)

- [Dual-branch Graph Domain Adaptation for Cross-scenario Multi-modal Emotion Recognition](https://mlsanity.com/paper/2603.26840/) (2026)
- [PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment](https://mlsanity.com/paper/2609.02231/) (2026)
- [Debiased Multimodal Personality Understanding through Dual Causal Intervention](https://mlsanity.com/paper/2605.06371/) (2026)
- [Knowing You at First Glance: Inferring Apparent Personality from Faces](https://mlsanity.com/paper/2607.14631/) (2026)

## Abstract (arXiv)

In recent years, predicting Big Five personality traits from multimodal data has received significant attention in artificial intelligence (AI). However, existing computational models often fail to achieve satisfactory performance. Psychological research has shown a strong correlation between pose and personality traits, yet previous research has largely ignored pose data in computational models. To address this gap, we develop a novel multimodal dataset that incorporates full-body pose data. The dataset includes video recordings of 287 participants completing a virtual interview with 36 questions, along with self-reported Big Five personality scores as labels. To effectively utilize this multimodal data, we introduce the Psychology-Inspired Network (PINet), which consists of three key modules: Multimodal Feature Awareness (MFA), Multimodal Feature Interaction (MFI), and Psychology-Informed Modality Correlation Loss (PIMC Loss). The MFA module leverages the Vision Mamba Block to capture comprehensive visual features related to personality, while the MFI module efficiently fuses the multimodal features. The PIMC Loss, grounded in psychological theory, guides the model to emphasize different modalities for different personality dimensions. Experimental results show that the PINet outperforms several state-of-the-art baseline models. Furthermore, the three modules of PINet contribute almost equally to the model's overall performance. Incorporating pose data significantly enhances the model's performance, with the pose modality ranking mid-level in importance among the five modalities. These findings address the existing gap in personality-related datasets that lack full-body pose data and provide a new approach for improving the accuracy of personality prediction models, highlighting the importance of integrating psychological insights into AI frameworks.