Facial Action Units and Personality Traits: An Explorative Approach through Computer Vision
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Facial action units (FAUs) are the visually discriminative anatomical building blocks of facial expressions. They provide essential cues for understanding facial behaviour and its potential links to personality traits. Aims: This study explores whether personality traits are reflected in facial behaviour during dyadic interactions by accurately capturing it and examining FAUs and their associations with the Big Five personality traits. Settings and Design: A customised big five personality-based interview was conducted in a smart room with 113 individuals to analyse their FAUs and correlate them with personality traits. Methods: FAUs were calibrated through OpenFace 2.0, a computer vision platform based on facial action coding system. Participants’ facial behaviour was recorded during the interviews, and FAUs were analysed in relation to their Big Five personality test scores. Statistical Analysis Used: Correlation analysis was performed to examine associations between FAUs and personality traits. Results: The study revealed a detectable pattern, with at least seven FAUs showing significant associations with Big Five personality traits. Conclusions: This study examined and generated a feasible hypothesis regarding the potential for personality research through facial cues within the framework of artificial intelligence. These findings pave the way for future hypothesis-driven research to generalise the initial insights, with implications for designing advanced assessment methodologies in personnel selection, clinical assessment and national security within behavioural science.
Authors: Sibin Raj K, Manas K Mandal
Institution: Rashtriya Raksha University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411745166