@article{Zhu2025, 
author = {Biaokai Zhu and Lu li and Xiaochun Hu and Fulin Wu and Zhaojie Zhang and Shengnan Zhu and Yanxi Wang and Jiali Wu and Jie Song and Feng Li and Sanman Liu and Jumin Zhao},
title = {DEFOG: Deep Learning with Attention Mechanism Enabled Cross-Age Face Recognition},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {3},
pages = {1342-1358},
keywords = {deep learning, FaceNet, cross-age face recognition},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010107},
doi = {10.26599/TST.2024.9010107},
abstract = {As individuals age, their facial features change, which can hinder the accuracy of face recognition technology. To address this challenge, a new cross-age face recognition algorithm, leveraging deep learning and a loss function (Loss), has been proposed in this article. The Retinaface algorithm detects faces in images, while the Resnet-50 model is enhanced by incorporating an attention mechanism and improved softmax loss (Arcface) to extract facial features. This approach has been tested on publicly available and custom-built datasets, and its performance has been compared to other cross-age face recognition techniques. The results show that the model effectively recognizes faces across different age groups.}
}