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

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.