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

2026-01-04

arXiv.org

AI Summary

This study addresses the challenges of infant and toddler facial recognition, where rapid facial development leads to low accuracy and unstable cross-temporal features, compounded by a lack of high-quality longitudinal datasets. It presents the first systematic evaluation of FaceNet, ArcFace, MagFace, and CosFace on longitudinal data from children aged 0–3 years, revealing a significant improvement in true acceptance rate (TAR) with age—from 30.7% at 0–6 months to 64.7% at 2.5–3 years (at FAR=0.1%). To mitigate embedding drift across time, the work proposes integrating a Domain-Adversarial Neural Network (DANN) to enhance temporal consistency of facial features, which boosts cross-temporal verification TAR by over 12%, substantially improving both stability and accuracy in infant facial recognition.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Face recognition for infants and toddlers presents unique challenges due to rapid facial morphology changes, high inter-class similarity, and limited dataset availability. This study evaluates the performance of four deep learning-based face recognition models FaceNet, ArcFace, MagFace, 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 examines recognition accuracy across 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. Performance improves significantly in older children, reaching 64.7% TAR at 0.1% FAR in the 2.5 to 3 year age group. We also evaluate verification performance over different time intervals, revealing that shorter time gaps result in higher accuracy due to reduced embedding drift. To mitigate this drift, we apply a Domain Adversarial Neural Network (DANN) approach that improves TAR by over 12%, yielding 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 highlight the importance of future research on privacy preserving biometric authentication systems that can address temporal variability, particularly in secure and regulated urban environments where child verification is essential.

Problem

• infant face recognition
• developmental stages
• temporal variability
• biometric authentication
• facial morphology changes

Innovation

• longitudinal dataset
• temporal embedding drift
• Domain Adversarial Neural Network (DANN)
• infant face recognition
• developmental stage evaluation