Towards a Deeper Insight Into Face Detection in Neonatal Wards

Neonatal face detection is the prerequisite for face-based intelligent medical applications. Nevertheless, it has been found that this area has received minimal attention in existing research. The paucity of open-source, large-scale datasets significantly constrains current studies, which are further compounded by issues such as large-scale occlusions, class imbalance, and precise localization requirements. This work aims to address these challenges from both data and methodological perspectives. We constructed the first open-source face detection dataset for neonates, involving images from 1,000 neonates in the neonatal wards. Utilizing this dataset and adopting NICUface-RF as the baseline, we introduce two novel modules. The hierarchical contextual classification aims to improve the positive/negative anchor ratios and alleviate large-scale occlusions. Concurrently, the DIoU-aware NMS is designed to preserve bounding boxes of superior localization quality by employing predicted DIoUs as the ranking criterion in NMS procedures. Experimental results illustrate the superiority of our method. The dataset and code is available at https://github.com/neonatal-pain .