用风格迁移提升虹膜识别精度并保护隐私
Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer
- 提取虹膜风格特征，增强对旋转等变化的鲁棒性
- 识别准确率显著高于传统方法，验证风格特征有效性
- 通过风格迁移隐藏敏感信息，适合隐私敏感场景
虹膜纹理被视为身份认证的金标准生物特征。随着对鲁棒虹膜识别方法的需求上升及虹膜攻击带来的安全与隐私担忧加剧，本文受神经风格迁移启发，提出虹膜纹理的风格特征可作为可靠识别基础，且对旋转、视角变化等更鲁棒。实验表明，相比传统特征，该方法分类准确率显著提升。进一步提出利用神经风格迁移模糊可识别的虹膜风格特征，在保护敏感生物特征的同时，仍保留眼图像在眼分割与注视估计等任务中的可用性。本工作为面向虹膜、安全且隐私友好的生物特征系统开辟了新路径。
原文摘要 · Abstract (English)
Iris texture is widely regarded as a gold standard biometric modality for authentication and identification. The demand for robust iris recognition methods, coupled with growing security and privacy concerns regarding iris attacks, has escalated recently. Inspired by neural style transfer, an advanced technique that leverages neural networks to separate content and style features, we hypothesize that iris texture's style features provide a reliable foundation for recognition and are more resilient to variations like rotation and perspective shifts than traditional approaches. Our experimental results support this hypothesis, showing a significantly higher classification accuracy compared to conventional features. Further, we propose using neural style transfer to obfuscate the identifiable iris style features, ensuring the protection of sensitive biometric information while maintaining the utility of eye images for tasks like eye segmentation and gaze estimation. This work opens new avenues for iris-oriented, secure, and privacy-aware biometric systems.
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