Evaluating Deep Learning-Based Face Recognition for Infants and Toddlers: Impact of Age Across Developmental Stages

Face recognition for infants and toddlers presents unique challenges due to rapid facial morphology changes, high inter-class similarity, and the limited availability of datasets. This study evaluates the performance of four deep learning-based face recognition models—FaceNet, ArcFace, Mag-Face, and CosFace—on a newly developed longitudinal dataset collected over a 24-month period in seven sessions involving children aged 0 to 3 years. Our analysis investigates recognition accuracy across multiple developmental stages, showing that the True Accept Rate (TAR) is only 30.7% at 0.1% False Accept Rate (FAR) for infants aged 0 to 6 months due to unstable facial features, but improves significantly in older children, reaching 64.7% TAR at 0.1% FAR in the 2.5 to 3 year age group. We also examine how face verification performance changes in different time intervals, revealing that shorter time gaps produce better accuracy due to reduced embedding drift. To mitigate this drift, we apply Domain-Adversarial Neural Network (DANN) strategy that improves TAR by more than 12% and yields features that are more temporally stable and generalizable. These findings are critical for building biometric systems that function reliably over time in smart city applications such as public healthcare, child safety, and digital identity services. The challenges observed in early age groups also highlight the importance of future research on privacy-preserving biometric authentication systems that can address temporal variability, especially in secure and regulated urban environments where child verification is vital.