Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition
This work addresses the challenge of accurately recognizing real personality traits from expressive behaviors, which is important for applications in human-computer interaction and psychology, though it appears incremental in its methodological contributions.
The paper tackles the problem of real personality recognition (RPR) by proposing a method that simulates personalized internal cognition from external audio-visual behaviors, achieving improved recognition performance over existing observer-based approaches.
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.