# Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No

arXiv: https://arxiv.org/abs/1911.06396 · HTML: https://mlsanity.com/paper/1911.06396/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 16 of 16)

- [Towards Fair Federated Learning with Zero-Shot Data Augmentation](https://mlsanity.com/paper/2104.13417/) (2021)
- [Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology](https://mlsanity.com/paper/2112.14656/) (2021)
- [AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark](https://mlsanity.com/paper/2406.00783/) (2024)
- [The Gender Gap in Face Recognition Accuracy Is a Hairy Problem](https://mlsanity.com/paper/2206.04867/) (2022)
- [Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning](https://mlsanity.com/paper/2302.11102/) (2023)
- [What Should Be Balanced in a "Balanced" Face Recognition Dataset?](https://mlsanity.com/paper/2304.09818/) (2023)
- [Synthetic Data for the Mitigation of Demographic Biases in Face Recognition](https://mlsanity.com/paper/2402.01472/) (2024)
- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness](https://mlsanity.com/paper/2211.05809/) (2022)
- [Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification](https://mlsanity.com/paper/2309.04447/) (2023)
- [Does Face Recognition Error Echo Gender Classification Error?](https://mlsanity.com/paper/2104.13803/) (2021)
- [The Casual Conversations v2 Dataset](https://mlsanity.com/paper/2303.04838/) (2023)
- [Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms](https://mlsanity.com/paper/2406.06932/) (2024)
- [CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems](https://mlsanity.com/paper/2407.13922/) (2024)
- [Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset](https://mlsanity.com/paper/2111.00660/) (2021)
- [A Rapid Test for Accuracy and Bias of Face Recognition Technology](https://mlsanity.com/paper/2502.14996/) (2025)

## Abstract (arXiv)

Previous studies generally agree that face recognition accuracy is higher for older persons than for younger persons. But most previous studies were before the wave of deep learning matchers, and most considered accuracy only in terms of the verification rate for genuine pairs. This paper investigates accuracy for age groups 16-29, 30-49 and 50-70, using three modern deep CNN matchers, and considers differences in the impostor and genuine distributions as well as verification rates and ROC curves. We find that accuracy is lower for older persons and higher for younger persons. In contrast, a pre deep learning matcher on the same dataset shows the traditional result of higher accuracy for older persons, although its overall accuracy is much lower than that of the deep learning matchers. Comparing the impostor and genuine distributions, we conclude that impostor scores have a larger effect than genuine scores in causing lower accuracy for the older age group. We also investigate the effects of training data across the age groups. Our results show that fine-tuning the deep CNN models on additional images of older persons actually lowers accuracy for the older age group. Also, we fine-tune and train from scratch two models using age-balanced training datasets, and these results also show lower accuracy for older age group. These results argue that the lower accuracy for the older age group is not due to imbalance in the original training data.