Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer
arXiv:2503.04707 · doi:10.1145/3729413
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 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.
The 2025 ACM Symposium on Eye Tracking Research & Applications (ETRA)
References in corpus (10)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Revisiting Batch Normalization For Practical Domain Adaptation
- Artistic style transfer for videos
- RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
- OpenEDS: Open Eye Dataset
- The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars
- Exploring Deep Learning Image Super-Resolution for Iris Recognition
- Deep Learning for Iris Recognition: A Review
- Privacy-Preserving Eye Videos using Rubber Sheet Model