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DEFOG: Deep Learning with Attention Mechanism Enabled Cross-Age Face Recognition
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DOI：10.26599/TST.2024.9010107.png)
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Abstract
En 中文
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.
Keywords:
Deep learning
Attention mechanisms
Accuracy
Embedded systems
Face recognition
Feature extraction
Data models
Complexity theory
Facial features
Residual neural networks
FaceNet
deep learning
cross-age face recognition
Journal
T
IF:
3.5
Papers:
987
Citations:
2.5K
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Cited Papers