Impact of Image Resolution on Age Estimation with DeepFace and InsightFace
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Abstract (arXiv)
Automatic age estimation is widely used for age verification, where input images often vary considerably in resolution. This study evaluates the effect of image resolution on age estimation accuracy using DeepFace and InsightFace. A total of 1000 images from the IMDB-Clean dataset were processed in seven resolutions, resulting in 7000 test samples. Performance was evaluated using Mean Absolute Error (MAE), Standard Deviation (SD), and Median Absolute Error (MedAE). Based on this study, we conclude that input image resolution has a clear and consistent impact on the accuracy of age estimation in both DeepFace and InsightFace. Both frameworks achieve optimal performance at 224x224 pixels, with an MAE of 10.83 years (DeepFace) and 7.46 years (InsightFace). At low resolutions, MAE increases substantially, while very high resolutions also degrade accuracy. InsightFace is consistently faster than DeepFace across all resolutions.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 18 Nov 2025.
Builds on
6 of 20 references.
References · 6
- 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
- 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 429
- 2021FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wildcited by 20
- 2021Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusioncited by 13
- 2017Deep Convolutional Neural Network for Age Estimation based on VGG-Face Modelcited by 11
- 2018Fusion Network for Face-based Age Estimationcited by 3
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Citing papers per year: 2026: 1.
1 paper.