Multimodal Personality Prediction: A Real-Time Recognition System for Social Robots with Data Acquisition

Abstract

In this paper, we propose a new real-time recognition system that predicts the Big Five personality traits extroversion, agreeableness, conscientiousness, neuroticism and openness. This system continuously evaluates these traits over time and across various context. By treating each moment individually to predict personality scores, we have implemented and compared various multimodal approaches to enhance the accuracy of these predictions. Our framework has shown the capability to obtain robust personality predictions extrapolated from complex information. Additionally, we have successfully implemented this framework in a real robot, confirming its potential applicability in the realm of social robotics. Based on these research findings, our personality prediction model is expected to operate stably in a wide range of environments, contributing to social interactions and applications.