通过外在行为推断内在认知，更准确识别真实人格。
Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition
- 从短时音视频行为模拟个体化内在认知，构建个性化网络权重。
- 将认知表示为二维图结构，用新型2D-GNN提取特征进行人格推断。
- 端到端训练提升真实人格识别效果，适合行为分析与心理学研究。
自动真实人格识别（RPR）旨在从个体的表达行为中评估其真实人格特质。然而，现有方法通常作为外部观察者，基于目标个体的表达行为推断观察者的性格印象，这与真实人格偏差较大，导致识别性能不佳。受真实人格与生成表达行为背后的内在认知之间关联的启发，本文提出一种新颖的RPR方法：从易获取的目标个体短时音视频行为中高效模拟个性化内在认知。所模拟的个性化认知以一组网络权重形式表示，使个性化网络能复现个体特定的面部反应。这些权重进一步被编码为包含二维节点与边特征矩阵的新颖图结构，并设计了一种新型二维图神经网络（2D-GNN）从中推断真实人格特质。为有效模拟与人格相关的真实认知，提出端到端策略，联合训练认知模拟、二维图构建及人格识别模块，显著提升识别精度。
原文摘要 · Abstract (English)
Automatic real personality recognition (RPR) aims to evaluate human real personality traits from their expressive behaviours. However, most existing solutions generally act as external observers to infer observers' personality impressions based on target individuals' expressive behaviours, which significantly deviate from their real personalities and consistently lead to inferior recognition performance. Inspired by the association between real personality and human internal cognition underlying the generation of expressive behaviours, we propose a novel RPR approach that efficiently simulates personalised internal cognition from easy-accessible external short audio-visual behaviours expressed by the target individual. The simulated personalised cognition, represented as a set of network weights that enforce the personalised network to reproduce the individual-specific facial reactions, is further encoded as a novel graph containing two-dimensional node and edge feature matrices, with a novel 2D Graph Neural Network (2D-GNN) proposed for inferring real personality traits from it. To simulate real personality-related cognition, an end-to-end strategy is designed to jointly train our cognition simulation, 2D graph construction, and personality recognition modules.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。