OBBabyFace: Oriented Bounding Box for Infant Face Detection

This study presents an infant-specific face detection approach that addresses the existing gap in facial detection for non-adults, where the typical bias is toward adult faces. A new infant faces dataset, which will be made available to the public upon request, was created to enhance Deep Learning (DL) models’ ability to accurately detect infant faces, comprising over 8,882 images with diverse orientations. We introduce Oriented Bounding Boxes (OBB) to account for greater variability in face orientations observed in infants, offering precise alignment to their orientation, a significant improvement over traditional Axis-Aligned Bounding Boxes (AABB). Employing the YOLOv8-OBB architecture, our model is trained and compared against state-of-the-art models such as RetinaFace and MogFace. The results show that our approach outperforms state-of-the-art methods in precision and recall, particularly in non-frontal facial orientations. The proposed infant face detector marks a major advancement in pediatric face detection technology, offering a robust foundation for future advancements in medical monitoring and developmental diagnosis.