Open Access

ARTICLE

Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

Yonghang Yan¹, Xin Xie¹, Hengyi Ren², Ying Cao^1,*, Hongwei Chang³

1 Henan Key Laboratory of Big Data Analysis and Processing, Computer and Information Engineering, Henan University, Kaifeng, 475004, China
2 College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037, China
3 Henan Branch, China Life Insurance Co., Ltd., Zhengzhou, 450000, China

* Corresponding Author: Ying Cao. Email:

Computers, Materials & Continua 2025, 82(3), 5035-5055. https://doi.org/10.32604/cmc.2025.058276

Received 09 September 2024; Accepted 17 December 2024; Issue published 06 March 2025

Abstract

Fingerprint features, as unique and stable biometric identifiers, are crucial for identity verification. However, traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks, potentially leading to user data leakage. Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data, effectively addressing privacy and security concerns. However, variations in fingerprint data due to factors such as region, ethnicity, sensor quality, and environmental conditions result in significant heterogeneity across clients. This heterogeneity adversely impacts the generalization ability of the global model, limiting its performance across diverse distributions. To address these challenges, we propose an Adaptive Federated Fingerprint Recognition algorithm (AFFR) based on Federated Learning. The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model, adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics. Additionally, a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates. Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection, showcasing its strong application potential and competitiveness in heterogeneous data environments.

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Keywords

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