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.

翻译：虹膜纹理被广泛视为身份认证与识别的黄金标准生物特征模态。近年来，对鲁棒虹膜识别方法的需求，以及对虹膜攻击日益增长的安全与隐私担忧，已显著加剧。受神经风格迁移这一利用神经网络分离内容与风格特征的先进技术启发，我们提出假设：虹膜纹理的风格特征为识别提供了可靠基础，且相较于传统方法，对旋转和视角变化等干扰具有更强的鲁棒性。我们的实验结果支持这一假设，显示其分类准确率显著高于传统特征。此外，我们提出利用神经风格迁移来掩蔽可识别的虹膜风格特征，从而在保护敏感生物特征信息的同时，维持眼部图像在眼部分割和视线估计等任务中的可用性。这项工作为面向虹膜的、安全且具备隐私意识的生物特征系统开辟了新途径。