# Face Recognition In Children: A Longitudinal Study

arXiv: https://arxiv.org/abs/2204.01760 · HTML: https://mlsanity.com/paper/2204.01760/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Face.evoLVe: A High-Performance Face Recognition Library](https://mlsanity.com/paper/2107.08621/) (2021)
- [Longitudinal Study of Child Face Recognition](https://mlsanity.com/paper/1711.03990/) (2017)
- [Large age-gap face verification by feature injection in deep networks](https://mlsanity.com/paper/1602.06149/) (2016)
- [Iris Recognition Performance in Children: A Longitudinal Study](https://mlsanity.com/paper/2101.06346/) (2021)
- [BEAT: An Open-Source Web-Based Open-Science Platform](https://mlsanity.com/paper/1704.02319/) (2017)

## Built on by (most cited first, 7 of 7)

- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark](https://mlsanity.com/paper/2304.11685/) (2023)
- [Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition](https://mlsanity.com/paper/2301.05776/) (2023)
- [Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age](https://mlsanity.com/paper/2408.07225/) (2024)
- [ChildDiffusion: Unlocking the Potential of Generative AI and Controllable Augmentations for Child Facial Data using Stable Diffusion and Large Language Models](https://mlsanity.com/paper/2406.11592/) (2024)
- [Mitigating Longitudinal Performance Degradation in Child Face Recognition Using Synthetic Data](https://mlsanity.com/paper/2601.01689/) (2026)
- [Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction](https://mlsanity.com/paper/2604.06961/) (2026)

## Abstract (arXiv)

The lack of high fidelity and publicly available longitudinal children face datasets is one of the main limiting factors in the development of face recognition systems for children. In this work, we introduce the Young Face Aging (YFA) dataset for analyzing the performance of face recognition systems over short age-gaps in children. We expand previous work by comparing YFA with several publicly available cross-age adult datasets to quantify the effects of short age-gap in adults and children. Our analysis confirms a statistically significant and matcher independent decaying relationship between the match scores of ArcFace-Focal, MagFace, and Facenet matchers and the age-gap between the gallery and probe images in children, even at the short age-gap of 6 months. However, our result indicates that the low verification performance reported in previous work might be due to the intra-class structure of the matcher and the lower quality of the samples. Our experiment using YFA and a state-of-the-art, quality-aware face matcher (MagFace) indicates 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 Months age-gaps, respectively, suggesting that face recognition may be feasible for children for age-gaps of up to three years.