# Longitudinal Study of Child Face Recognition

arXiv: https://arxiv.org/abs/1711.03990 · HTML: https://mlsanity.com/paper/1711.03990/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 18 of 18)

- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Towards Fair Face Verification: An In-depth Analysis of Demographic Biases](https://mlsanity.com/paper/2307.10011/) (2023)
- [Bias and Diversity in Synthetic-based Face Recognition](https://mlsanity.com/paper/2311.03970/) (2023)
- [Face Recognition In Children: A Longitudinal Study](https://mlsanity.com/paper/2204.01760/) (2022)
- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark](https://mlsanity.com/paper/2304.11685/) (2023)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [Longitudinal Analysis of Mask and No-Mask on Child Face Recognition](https://mlsanity.com/paper/2111.00121/) (2021)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [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)
- [MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition](https://mlsanity.com/paper/2408.16563/) (2024)
- [Longitudinal Performance of Iris Recognition in Children: Time Intervals up to Six years](https://mlsanity.com/paper/2303.12720/) (2023)
- [Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations](https://mlsanity.com/paper/2201.03045/) (2022)
- [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)
- [Face De-Identification: A Domain-Centric Survey from Capture to Processing](https://mlsanity.com/paper/2607.25926/) (2026)

## Abstract (arXiv)

We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2, 18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3, 18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction.