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

TL;DR

This paper introduces a novel approach for real personality recognition by simulating internal cognition from external expressive behaviours using a 2D Graph Neural Network, improving the accuracy of personality trait inference.

Contribution

The paper presents a new method that models internal cognition from external behaviours and employs a 2D-GNN for more accurate real personality recognition.

Findings

Effective simulation of internal cognition from external behaviours.

Improved accuracy in real personality trait recognition.

Novel 2D-GNN architecture for personality inference.

Abstract

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.