Robust Neonatal Face Detection in Real-world Clinical Settings
This work addresses a domain-specific problem for automated systems in healthcare, such as pain recognition, by enabling robust and real-time neonatal face detection, though it is incremental as it adapts an existing method to new data.
The paper tackles the problem of detecting neonatal faces in challenging clinical settings like the NICU, where existing face detection models perform poorly, achieving 68.7% accuracy compared to 7.37% for an off-the-shelf solution.
Current face detection algorithms are extremely generalized and can obtain decent accuracy when detecting the adult faces. These approaches are insufficient when handling outlier cases, for example when trying to detect the face of a neonate infant whose face composition and expressions are relatively different than that of the adult. It is furthermore difficult when applied to detect faces in a complicated setting such as the Neonate Intensive Care Unit. By training a state-of-the-art face detection model, You-Only-Look-Once, on a proprietary dataset containing labelled neonate faces in a clinical setting, this work achieves near real time neonate face detection. Our preliminary findings show an accuracy of 68.7%, compared to the off the shelf solution which detected neonate faces with an accuracy of 7.37%. Although further experiments are needed to validate our model, our results are promising and prove the feasibility of detecting neonatal faces in challenging real-world settings. The robust and real-time detection of neonatal faces would benefit wide range of automated systems (e.g., pain recognition and surveillance) who currently suffer from the time and effort due to the necessity of manual annotations. To benefit the research community, we make our trained weights publicly available at github(https://github.com/ja05haus/trained_neonate_face).