Beyond Accuracy: Fairness, Scalability, and Uncertainty Considerations in Facial Emotion Recognition

Facial emotion recognition (FER) from images or videos is an emerging subfield of emotion recognition that in recent years has achieved increased traction resulting in a wide range of models, datasets, and applications. Benchmarking computer vision methods often provide accuracy rates above 90% in controlled settings. However, little focus has been given to aspects of fairness, uncertainty, and scalability within facial emotion recognition systems. The increasing applicability of FER models within assisted psychiatry and similar domains underlines the importance of fair and computational resource compliant decision-making. The primary objective of this paper is to propose methods for assessment of existing open source FER models to establish a thorough understanding of their current fairness, scalability, and robustness.