# Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer

arXiv: https://arxiv.org/abs/2503.04707 · HTML: https://mlsanity.com/paper/2503.04707/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Revisiting Batch Normalization For Practical Domain Adaptation](https://mlsanity.com/paper/1603.04779/) (2016)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [Demystifying Neural Style Transfer](https://mlsanity.com/paper/1701.01036/) (2017)
- [Coherent Online Video Style Transfer](https://mlsanity.com/paper/1703.09211/) (2017)
- [Artistic style transfer for videos](https://mlsanity.com/paper/1604.08610/) (2016)
- [RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking](https://mlsanity.com/paper/1910.00694/) (2019)
- [Characterizing and Improving Stability in Neural Style Transfer](https://mlsanity.com/paper/1705.02092/) (2017)
- [OpenEDS: Open Eye Dataset](https://mlsanity.com/paper/1905.03702/) (2019)
- [ReCoNet: Real-time Coherent Video Style Transfer Network](https://mlsanity.com/paper/1807.01197/) (2018)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art](https://mlsanity.com/paper/1804.00194/) (2018)
- [Real-Time Gaze Tracking with Event-Driven Eye Segmentation](https://mlsanity.com/paper/2201.07367/) (2022)
- [OpenEDS2020: Open Eyes Dataset](https://mlsanity.com/paper/2005.03876/) (2020)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [An Experimental Study of Deep Convolutional Features For Iris Recognition](https://mlsanity.com/paper/1702.01334/) (2017)
- [Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?](https://mlsanity.com/paper/2002.08916/) (2020)
- [The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars](https://mlsanity.com/paper/2003.04250/) (2020)
- [Privacy-Preserving Eye Videos using Rubber Sheet Model](https://mlsanity.com/paper/2004.01792/) (2020)
- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [Recognition Oriented Iris Image Quality Assessment in the Feature Space](https://mlsanity.com/paper/2009.00294/) (2020)
- [Exploring Deep Learning Image Super-Resolution for Iris Recognition](https://mlsanity.com/paper/2311.01241/) (2023)

## Built on by (most cited first, 1 of 1)

- [Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study](https://mlsanity.com/paper/2504.10267/) (2025)

## Abstract (arXiv)

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.