InfantFace: Detecting infant faces in neonatal clinical environments

2026-06-18

AI Summary

This study addresses the performance limitations of existing general-purpose face detection models in neonatal clinical settings, where occlusion, variable lighting, and cluttered backgrounds pose significant challenges. To overcome these issues, the authors present the first adaptation of YOLOv11m to this domain, leveraging cross-domain pretraining on multiple datasets—VGGFace2, CelebA, FDDB, and WIDER FACE—followed by fine-tuning on a clinical video dataset comprising 113 neonates to achieve domain adaptation. The model achieves an AP50 of 0.87 prior to fine-tuning, outperforming mainstream general-purpose detectors; after fine-tuning on the clinical data, AP50 further improves to 0.96, substantially enhancing both robustness and accuracy in detecting neonatal faces under complex real-world conditions.

Abstract

Reliable localisation of the neonatal face is the first step for several video-camera based non-contact assessments such as pain and distress related facial expression analysis, pain scoring, cardiorespiratory signal extraction and cessation of breathing alerts. However, major challenges persist in neonatal clinical environments. Cluttered backgrounds, illumination changes and poor lighting conditions can reduce the accuracy of face detection models. Clinical interventions, monitoring equipment and, in some cases, medical devices can obstruct the face, making visual assessment difficult. We propose a one-stage YOLOv11m-based model tailored for face detection of infants in neonatal clinical environments. We combined multiple publicly available datasets (VGGFace2, CelebA, FDDB, WIDER FACE) to train and evaluate our proposed model. We then fine-tuned our model on a neonatal research dataset involving 228 videos from 114 recording sessions of 113 independent infants. Before fine-tuning, our model achieved an AP50 of 0.87, surpassing the performance of three state-of-the-art general face detectors. Performance improved further to an AP50 of 0.96 after clinical-domain adaptation. Evaluating face detection performance across different datasets remains a challenge due to the lack of publicly available neonatal datasets. Prioritising the creation of such datasets, while upholding appropriate privacy safeguards and ethical standards in their creation and use, would greatly support further progress in this field.

Problem

• infant face detection
• neonatal clinical environment
• face occlusion
• low-light conditions
• cluttered background

Innovation

• InfantFace
• YOLOv11m
• neonatal face detection
• clinical-domain adaptation
• non-contact assessment