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.

翻译：婴幼儿人脸识别面临独特挑战，主要源于面部形态快速变化、类间相似度高以及数据集稀缺。本研究基于新构建的纵向数据集，评估了四种深度学习人脸识别模型（FaceNet、ArcFace、MagFace、CosFace）在0-3岁儿童群体中的性能。该数据集通过七次采集会话覆盖24个月周期。分析表明：由于面部特征不稳定，0-6月龄婴儿在误接受率0.1%条件下的真接受率仅为30.7%；随着年龄增长，2.5-3岁组在同等误接受率下真接受率显著提升至64.7%。通过不同时间间隔的验证性能评估发现，较短时间间隔因嵌入向量漂移减弱而获得更高准确率。为抑制特征漂移，本研究采用领域对抗神经网络方法，使真接受率提升超12%，获得更具时间稳定性与泛化能力的特征表示。这些发现对构建适用于智慧城市场景（如公共医疗、儿童安全、数字身份服务）的长期可靠生物识别系统至关重要。低龄组识别难题凸显了未来研究的重点：需开发能应对时间变异性的隐私保护型生物认证系统，这在儿童身份核验至关重要的安全规范型城市环境中具有特殊意义。