Abstract
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 mask 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.
Abstract (translated)
虹膜纹理被广泛认为是身份认证和识别的黄金标准生物特征模态。随着对可靠虹膜识别方法的需求日益增加，以及关于虹膜攻击的安全性和隐私问题的关注加剧，最近人们对这一领域产生了更大的兴趣。受神经风格迁移（一种利用神经网络分离内容与风格特征的先进技术）启发，我们假设虹膜纹理中的风格特征可以为识别提供一个可靠的依据，并且比传统方法更能抵抗旋转和透视变化带来的影响。我们的实验结果支持了这一假设，显示出相比传统的特征提取方法，分类准确率有了显著提升。 此外，我们提出使用神经风格迁移来掩盖可识别人脸风格的虹膜特征，以确保敏感生物识别信息的安全性，同时保持眼睛图像在诸如眼球分割和注视估计等任务中的实用性。这项工作为面向虹膜、安全且隐私意识强的生物识别系统开辟了新的途径。
URL
https://arxiv.org/abs/2503.04707
