APEX: Attention on Personality based Emotion ReXgnition Framework

TL;DR

The paper introduces APEX, a novel emotion recognition framework that incorporates personality traits to improve accuracy by reweighing classifier outputs based on personality correlations, demonstrated on the ASCERTAIN dataset.

Contribution

It presents a new method integrating personality information into emotion recognition, addressing the challenge of user-specific variability in physiological signal analysis.

Findings

APEX outperforms existing emotion recognition methods on the ASCERTAIN dataset.

Incorporating personality traits improves classification accuracy.

The framework effectively reweighs classifier outputs based on personality correlations.

Abstract

Automated emotion recognition has applications in various fields, such as human-machine interaction, healthcare, security, education, and emotion-aware recommendation/feedback systems. Developing methods to analyze human emotions accurately is essential to enable such diverse applications. Multiple studies have been conducted to explore the possibility of using physiological signals and machine-learning techniques to evaluate human emotions. Furthermore, internal factors such as personality have been considered and involved in emotion recognition. However, integrating personality that is user specific within traditional machine-learning methods that use user-agnostic large data sets has become a critical problem. This study proposes the APEX: attention on personality-based emotion recognition framework, in which multiple weak classifiers are trained on physiological signals of each participant's data, and the classification results are reweighed based on the personality correlations between corresponding subjects and test subjects. Experiments have been conducted on the ASCERTAIN dataset, and the results show that the proposed framework outperforms existing studies.