Abstract
Longitudinal face recognition in children remains challenging due to rapid and nonlinear facial growth, which causes template drift and increasing verification errors over time. This work investigates whether synthetic face data can act as a longitudinal stabilizer by improving temporal robustness of child face recognition models. Using an identity disjoint protocol on the Young Face Aging (YFA) dataset, we evaluate three settings: (i) pretrained MagFace embeddings without dataset specific fine-tuning, (ii) MagFace fine-tuned using authentic training faces only, and (iii) MagFace fine-tuned using a combination of authentic and synthetically generated training faces. Synthetic data is generated using StyleGAN2 ADA and incorporated exclusively within the training identities; a post generation filtering step is applied to mitigate identity leakage and remove artifact affected samples. Experimental results across enrollment verification gaps from 6 to 36 months show that synthetic-augmented fine tuning substantially reduces error rates relative to both the pretrained baseline and real only fine tuning. These findings provide a risk aware assessment of synthetic augmentation for improving identity persistence in pediatric face recognition.
Abstract (translated)
儿童纵向面部识别仍然面临挑战，因为快速且非线性的面部成长会导致模板漂移和随时间推移验证错误的增加。这项工作研究了合成面部数据是否可以通过提高儿童面部识别模型的时间鲁棒性来充当纵向稳定器。在Young Face Aging (YFA) 数据集上采用身份独立协议后，我们评估了三种设置：(i) 不经过特定数据集微调的预训练MagFace嵌入；(ii) 仅使用真实训练面孔对MagFace进行微调；(iii) 使用真实和合成生成的训练面孔组合对MagFace进行微调。利用StyleGAN2 ADA生成的合成数据仅包含在训练身份中，并且在生成后应用了一个过滤步骤以减轻身份泄漏并移除受影响的样本。实验结果显示，在6到36个月的注册验证间隔期间，合成增强型微调相对于预训练基线和仅使用真实面孔进行微调的情况显著降低了错误率。这些发现为提高儿童面部识别中身份持久性的合成数据增强方法的风险评估提供了依据。
URL
https://arxiv.org/abs/2601.01689
