Comparative Analysis of CNNs and Vision Transformers for Age Estimation
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12251Keywords:
Computer vision, CNNs, Vision transformers, VGG-16, ResNet-50, EfficientNet-B0, ViT, CaiT, SwinAbstract
Vision Transformers have recently gained significant importance in computer vision tasks due to their self-attention mechanisms.
Previously, CNNs dominated the computer vision field by achieving remarkable results in various applications such as image classification, object recognition, and more. However, with the arrival of Vision Transformers, an intense competition has emerged between the two. This paper presents a comparative analysis of the performance of CNNs and Vision Transformers for the task of age estimation on the FG-NET and UTKFace datasets. We performed age estimation using six models, including three CNN models (VGG-16, ResNet-50, EfficientNet-B0) and three Vision Transformer models (ViT, CaiT, Swin). Our experimental results show that the Swin Transformer outperformed both CNN and other Vision Transformers, achieving a mean absolute error (MAE) of 2.79 years on FG-NET and 4.37 years on UTKFace.
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Copyright (c) 2025 Waqar Tanveer, Laura Fernández-Robles, Eduardo Fidalgo, Víctor González-Castro, Enrique Alegre, Milad Mirjalili
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