Benchmarking Computer-Vision-Based Facial Emotion Classification Algorithms While Wearing Surgical Masks

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.