Benchmarking computer-vision-based facial emotion classification algorithms while wearing surgical masks

• Main Author: Coelho, Luis
• Publication Date: 2023
• Other Authors: Reis, Sara, Moreira, Cristina, Cardoso, Helena, Sequeira, Miguela, Coelho, Raquel
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.22/25421
• topic: Emotion perception; Facial emotion; Emotion classification; Surgical mask

Effective human communication relies heavily on emotions, making them a crucial aspect of interaction. As technology progresses, the desire for machines to exhibit more human-like characteristics, including emotion recognition, grows. DeepFace has emerged as a widely adopted library for facial emotion recognition. However, the widespread use of surgical masks after the COVID-19 pandemic presents a considerable obstacle to its performance. To assess this issue, we conducted a benchmark using the FER2013 dataset. The results revealed a substantial performance decline when individuals wore surgical masks. “Disgust” suffers a 22.6% F1-score reduction, while “Surprise” is least affected with a 48.7% reduction. Addressing these issues improves human–machine interfaces and paves the way for more natural machine communication.