用合成数据提升儿童人脸识别长期稳定性，降低随时间增长的误识率。
Mitigating Longitudinal Performance Degradation in Child Face Recognition Using Synthetic Data
- 通过合成数据增强训练，缓解儿童面部快速成长带来的模型性能下降。
- 在6至36个月验证间隔下，合成数据组误差率显著低于真实数据组和预训练基线。
- 适合关注儿童身份持久性、人脸识别长期鲁棒性的研究者与应用开发者。
儿童纵向人脸识别因面部快速且非线性生长而面临挑战，导致模板漂移和随时间推移的验证错误增加。本文研究合成人脸数据是否可作为纵向稳定器，提升儿童人脸识别模型的时间鲁棒性。在Young Face Aging（YFA）数据集上采用身份无关协议，评估三种设置：(i) 未经微调的预训练MagFace嵌入，(ii) 仅使用真实人脸微调的MagFace，(iii) 使用真实与合成人脸联合微调的MagFace。合成数据通过StyleGAN2 ADA生成，仅用于训练身份；生成后进行过滤以减少身份泄露和伪影样本。在6至36个月注册-验证间隔下的实验表明，合成数据增强微调显著降低误差率，优于预训练基线和纯真实数据微调。结果为合成数据在儿科人脸识别中提升身份持续性的风险评估提供了依据。
原文摘要 · Abstract (English)
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.
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