Real-time Ubiquitous Pain Recognition

Emotion recognition is a quickly growing field due to the increased interest in building systems which can classify and respond to emotions. Recent medical crises, such as the opioid overdose epidemic in the United States and the global COVID-19 pandemic has emphasized the importance of emotion recognition applications is areas like Telehealth services. Considering this, we propose an approach to real-time ubiquitous pain recognition from facial images. We have conducted offline experiments using the BP4D dataset, where we investigate the impact of gender and data imbalance. This paper proposes an affordable and easily accessible system which can perform pain recognition inferences. The results from this study found a balanced dataset, in terms of class and gender, results in the highest accuracies for pain recognition. We also detail the difficulties of pain recognition using facial images and propose some future work that can be investigated for this challenging problem.