标题： 从外部表现行为学习个性化的人类内部认知用于真实人格识别 显示英文标题

标题： Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition

摘要： 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的人格特征。 然而，大多数现有解决方案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人格印象，这与他们的真实人格存在显著偏差，并持续导致识别性能不佳。 受真实人格与生成表达行为背后人类内部认知之间关联的启发，我们提出了一种新的RPR方法，该方法能够从目标个体容易获取的外部短时音频-视觉行为中高效模拟个性化的内部认知。 模拟的个性化认知被表示为一组网络权重，这些权重使个性化网络能够再现个体特定的面部反应，并进一步编码为包含二维节点和边特征矩阵的新图，同时提出了一种新的二维图神经网络（2D-GNN）用于从中推断真实人格特征。 为了模拟与真实人格相关的认知，设计了一种端到端策略，联合训练我们的认知模拟、二维图构建和人格识别模块。 显示英文摘要

摘要： 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.