# DeepIris: Iris Recognition Using A Deep Learning Approach

arXiv: https://arxiv.org/abs/1907.09380 · HTML: https://mlsanity.com/paper/1907.09380/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition](https://mlsanity.com/paper/2108.01569/) (2021)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images](https://mlsanity.com/paper/2305.12596/) (2023)
- [Towards an efficient Iris Recognition System on Embedded Devices](https://mlsanity.com/paper/2210.13101/) (2022)
- [Verification system based on long-range iris and Graph Siamese Neural Networks](https://mlsanity.com/paper/2208.00785/) (2022)
- [Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation](https://mlsanity.com/paper/2212.12733/) (2022)
- [On the Adversarial Inversion of Deep Biometric Representations](https://mlsanity.com/paper/2304.05561/) (2023)
- [On the Feasibility of Creating Iris Periocular Morphed Images](https://mlsanity.com/paper/2408.13496/) (2024)

## Abstract (arXiv)

Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control. Different features and algorithms have been proposed for iris recognition in the past. In this paper, we propose an end-to-end deep learning framework for iris recognition based on residual convolutional neural network (CNN), which can jointly learn the feature representation and perform recognition. We train our model on a well-known iris recognition dataset using only a few training images from each class, and show promising results and improvements over previous approaches. We also present a visualization technique which is able to detect the important areas in iris images which can mostly impact the recognition results. We believe this framework can be widely used for other biometrics recognition tasks, helping to have a more scalable and accurate systems.