Face recognition technology and privacy protection methods based on deep learning

Jianxiang Ma

doi:10.1117/12.3025901

8 April 2024 Face recognition technology and privacy protection methods based on deep learning

Jianxiang Ma

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903H (2024) https://doi.org/10.1117/12.3025901
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Face recognition technology is developing rapidly, and the issue of personal privacy protection has become increasingly prominent. How to protect personal privacy while ensuring recognition accuracy has become the focus of research. This study uses deep learning as the basis of face recognition technology and extracts high-dimensional feature representation of face images through the training and learning of deep neural networks. At the same time, for privacy protection, this study proposes anonymization and desensitization technology, privacy protection generation model, distributed learning and federated learning, multi-factor authentication, and other methods. These methods aim to increase information entropy, reduce personal identifiability, and protect personal privacy. Research results show that the face recognition accuracy of this method is 91%-98%. Face recognition technology based on deep learning has made significant progress in improving recognition accuracy and increasing information entropy. This method provides a foundation for efficient and accurate face recognition, and also provides a more reliable means for personal privacy protection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianxiang Ma "Face recognition technology and privacy protection methods based on deep learning", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903H (8 April 2024); https://doi.org/10.1117/12.3025901

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