Infant Facial Expression Analysis: Towards a Real-Time Video Monitoring System Using R-CNN and HMM.

The manual monitoring of young infants suffering from diseases like reflux is significant, since infants can hardly articulate their feelings. In this work, we propose a video-based infant monitoring system for the analysis of infant expressions and states, approaching real-time performance. The expressions of interest consist of discomfort, unhappy, joy and neutral, whereas states include sleep, pacifier and open mouth. Benefiting from the expression analysis, the discomfort moments can also be used and correlated with a symptom-related disease, such as a reflux measurement for the diagnosis of gastroesophageal reflux. The system consists of three components: infant expressions and states detection, object tracking and detection compensation. The proposed system is based on combining expression detection using Fast R-CNN with a compensated detection using analyzing information from the previous frame and utilizing a Hidden Markov Model. The experimental results show a mean average precision of 81.9% and 84.8% for 4 infant expressions and 3 states evaluated with both clinical and daily datasets. Meanwhile, the average precision for discomfort detection achieves up to 90%.