# Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age

arXiv: https://arxiv.org/abs/2408.07225 · HTML: https://mlsanity.com/paper/2408.07225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 1 Aug 2024.

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

- [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)
- [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)
- [Face Recognition In Children: A Longitudinal Study](https://mlsanity.com/paper/2204.01760/) (2022)
- [Longitudinal Analysis of Mask and No-Mask on Child Face Recognition](https://mlsanity.com/paper/2111.00121/) (2021)

## Built on by (most cited first, 2 of 2)

- [OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation](https://mlsanity.com/paper/2505.05374/) (2025)
- [Mitigating Longitudinal Performance Degradation in Child Face Recognition Using Synthetic Data](https://mlsanity.com/paper/2601.01689/) (2026)

## Abstract (arXiv)

The need for reliable identification of children in various emerging applications has sparked interest in leveraging child face recognition technology. This study introduces a longitudinal approach to enrollment and verification accuracy for child face recognition, focusing on the YFA database collected by Clarkson University CITeR research group over an 8 year period, at 6 month intervals.