出版刊物：IEEE Transactions on Consumer Electronics

出版时间：2025年

内容摘要：
User-friendly palmprint recognition enhances the user experience and promotes widespread adoption of palmprint-based payment systems accross diverse consumer electronics. However, palmprints contain biometric identifiers that raise significant privacy concerns regarding identity protection. Once these original palmprint data are leaked, it will be misused by adversaries for malicious activities. Despite this growing concern, research on methods to obscure or anonymize palmprints remains largely unexplored. Therefore, this study proposes a novel approach for palmprint de-identification using generative adversarial networks, termed PD-GAN, to effectively generate de-identified palmprints and remove identifiable identity features from palmprint images while maintaining their utility, thereby reducing the risks of privacy leakage and enabling secure data sharing and utilization. To ensure the stability and controllability of the synthesis process, we innovatively introduced the SSIM loss function and a palmprint recognition loss function based on ResNet18. Additionally, we proposed three evaluation metrics including de-identification rate, SSIM, and PSNR, to assess the de-identification effectiveness of the generated palmprints and the quality of the synthesized images. On the PolyU dataset, the recognition accuracy of the palmprint images generated by PD-GAN exhibited a substantial decline while preserving usability of the images. Notably, the accuracy in the ResNet18 recognition model dropped sharply to 2%. Similarly, on the Tongji palmprint dataset, the recognition accuracy dropped to 14.06% under the same ResNet18 architecture. The experimental results demonstrate that the proposed PD-GAN effectively generates de-identified palmprints images to protect identity privacy while retaining sufficient discriminative features for new recognition tasks. This ensures the utility of de-identified palmprints in authentication systems without compromising privacy, offering a practical solution for secure palmprint-based applications.

友好易用的掌纹识别技术能有效提升用户体验，推动掌纹支付系统在各种消费电子设备中的普及。然而，掌纹包含的生物特征标识符引发了身份保护方面的重大隐私隐忧。一旦原始掌纹数据泄露，攻击者可能将其用于恶意活动。尽管这一风险日益受到关注，针对掌纹模糊化或匿名化的研究方法仍处于探索不足的状态。为此，本研究提出一种基于生成对抗网络的掌纹去标识创新方案PD-GAN，通过有效生成去标识掌纹图像，在保持图像可用性的同时消除可识别身份特征，从而降低隐私泄露风险，实现安全的数据共享与利用。

为确保合成过程的稳定性和可控性，我们创新性地引入SSIM损失函数和基于ResNet18的掌纹识别损失函数。此外，提出了包含去标识化率、结构相似性和峰值信噪比的三项评估指标，用以全面评估生成掌纹的去标识效果与合成图像质量。在PolyU数据集上的实验表明，经PD-GAN生成的掌纹图像在保持可用性的同时，其识别准确率出现显著下降——在ResNet18识别模型中的准确率急剧降至2%。同样，在同济大学掌纹数据集上，相同网络架构下的识别准确率也下降至14.06%。实验结果表明，PD-GAN能有效生成具备身份隐私保护能力的去标识掌纹图像，同时保留足够的判别特征以支持新的识别任务。这既确保了去标识掌纹在认证系统中的实用性，又不会牺牲隐私安全性，为基于掌纹的安全应用提供了切实可行的解决方案。