Making Machine Learning Accessible for Developmental Science: The Case of Automated Face Detection

The last decade has seen rapid advancements in machine learning, significantly transforming fields like cybersecurity and healthcare. Developmental science has been slower to adopt these technologies. Yet, machine learning holds immense potential to transform this field, enabling scalable and data-driven insights into developmental processes. Broader adoption is currently hindered by challenges in algorithm selection and technical implementation. We address these barriers by focusing on an area that has reached high sophistication from a machine learning perspective while also being of significant interest to developmental scientists: face detection. Face detection is crucial for analysing visual experiences through children's dynamic, first-person views. Automatising this process allows efficient handling of large egocentric datasets, enabling well-powered studies otherwise limited by labour-intensive manual annotation. Here, we systematically evaluated 13 state-of-the-art face detection algorithms (DeepFace library) using data from two increasingly common developmental methodologies involving children under 3 years of age: head-mounted eye-tracking in more structured settings (N = 20; n = 10 4-month-olds, n = 10 8-month-olds) and head-mounted cameras in naturalistic home environments (N = 10 18-29-month-olds). Benchmarking these algorithms against manual annotations revealed that YOLOv11Face (M) and RetinaFace consistently outperformed others in terms of precision and recall, exhibiting strong concordance with manual ratings, lower error, reduced systematic deviation and robust rank-order correlations with manual annotations. To facilitate broader adoption, we introduce an accessible face detection tool (TinyExplorer Detection App), promoting efficiency, scalability, and innovation in developmental science by widening access to machine learning.