# Mitigating Longitudinal Performance Degradation in Child Face Recognition Using Synthetic Data

arXiv: https://arxiv.org/abs/2601.01689 · HTML: https://mlsanity.com/paper/2601.01689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 4 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Longitudinal Study of Child Face Recognition](https://mlsanity.com/paper/1711.03990/) (2017)
- [Face Recognition In Children: A Longitudinal Study](https://mlsanity.com/paper/2204.01760/) (2022)
- [Privacy-Safe Iris Presentation Attack Detection](https://mlsanity.com/paper/2408.02750/) (2024)
- [Longitudinal Analysis of Mask and No-Mask on Child Face Recognition](https://mlsanity.com/paper/2111.00121/) (2021)
- [Deep Learning Approach for Ear Recognition and Longitudinal Evaluation in Children](https://mlsanity.com/paper/2408.01588/) (2024)
- [Post-Mortem Human Iris Segmentation Analysis with Deep Learning](https://mlsanity.com/paper/2408.03448/) (2024)
- [Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age](https://mlsanity.com/paper/2408.07225/) (2024)
- [Securing Biometric Data: Fully Homomorphic Encryption in Multimodal Iris and Face Recognition](https://mlsanity.com/paper/2408.14609/) (2024)

## Abstract (arXiv)

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.