  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).
