Modelling the Interplay of Eye-Tracking Temporal Dynamics and Personality for Emotion Detection in Face-to-Face Settings

TL;DR

This study develops a personality-aware multimodal framework that combines eye-tracking, personality traits, and contextual cues to improve emotion recognition accuracy in face-to-face interactions, emphasizing the importance of subjective and objective data integration.

Contribution

It introduces a novel neural model that fuses eye-tracking dynamics, personality assessments, and stimulus cues for enhanced emotion detection, distinguishing between perceived and felt emotions.

Findings

Stimulus cues significantly improve perceived emotion prediction (F1 up to 0.77).

Personality traits notably enhance felt emotion recognition (F1 up to 0.58).

The approach outperforms traditional SVM and baseline models.

Abstract

Accurate recognition of human emotions is critical for adaptive human-computer interaction, yet remains challenging in dynamic, conversation-like settings. This work presents a personality-aware multimodal framework that integrates eye-tracking sequences, Big Five personality traits, and contextual stimulus cues to predict both perceived and felt emotions. Seventy-three participants viewed speech-containing clips from the CREMA-D dataset while providing eye-tracking signals, personality assessments, and emotion ratings. Our neural models captured temporal gaze dynamics and fused them with trait and stimulus information, yielding consistent gains over SVM and literature baselines. Results show that (i) stimulus cues strongly enhance perceived-emotion predictions (macro F1 up to 0.77), while (ii) personality traits provide the largest improvements for felt emotion recognition (macro F1 up to 0.58). These findings highlight the benefit of combining physiological, trait-level, and contextual information to address the inherent subjectivity of emotion. By distinguishing between perceived and felt responses, our approach advances multimodal affective computing and points toward more personalized and ecologically valid emotion-aware systems.