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.
Abstract (translated)
婴儿和幼儿的面部识别面临着独特的挑战，因为他们的面部形态快速变化、类别间的相似度高以及可用数据量有限。本研究评估了四种基于深度学习的面部识别模型（FaceNet、ArcFace、MagFace 和 CosFace）在一项新开发的纵向数据集上的性能表现。该数据集是在七年时间里分七个阶段收集而成，涉及年龄从0到3岁的儿童。 我们的分析考察了不同发育阶段下的识别准确性，结果显示，在0.1%假阳性率（FAR）条件下，对于0至6个月大的婴儿，真实接受率（TAR）仅为30.7%，这是由于面部特征不稳定所致。随着儿童年龄的增长，性能显著提高，在2.5到3岁的年龄段中，TAR在同样的错误接受率下达到了64.7%。 我们还评估了不同时间间隔下的验证性能，发现较短的时间差会导致更高的准确性，因为这减少了嵌入漂移（即随着时间推移面部特征表示的变化）的影响。为了缓解这种漂移，我们应用了一种域对抗神经网络（DANN）方法，该方法将TAR提高了超过12%，生成了更具有时间稳定性和泛化能力的特性。 这些发现对于构建在智慧城市应用程序中可靠运行的生物识别系统至关重要，例如公共健康、儿童安全和数字身份服务。早期年龄组观察到的挑战突显了未来研究的重要性，特别是关于隐私保护型生物特征认证系统的研究，这类系统能够应对时间变化性问题，在需要严格监管的城市环境中尤为关键，尤其是在儿童验证至关重要的场合下。
URL
https://arxiv.org/abs/2601.01680
