# Adaptive Deep Iris Feature Extractor at Arbitrary Resolutions

arXiv: https://arxiv.org/abs/2407.08341 · HTML: https://mlsanity.com/paper/2407.08341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [L2-constrained Softmax Loss for Discriminative Face Verification](https://mlsanity.com/paper/1703.09507/) (2017)
- [Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation](https://mlsanity.com/paper/1811.09998/) (2018)
- [Ring loss: Convex Feature Normalization for Face Recognition](https://mlsanity.com/paper/1803.00130/) (2018)
- [Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition](https://mlsanity.com/paper/1807.05248/) (2018)
- [Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?](https://mlsanity.com/paper/2002.08916/) (2020)
- [ThirdEye: Triplet Based Iris Recognition without Normalization](https://mlsanity.com/paper/1907.06147/) (2019)
- [Iris super-resolution using CNNs: is photo-realism important to iris recognition?](https://mlsanity.com/paper/2210.13125/) (2022)
- [All-in-Focus Iris Camera With a Great Capture Volume](https://mlsanity.com/paper/2011.09908/) (2020)
- [Exploring Deep Learning Image Super-Resolution for Iris Recognition](https://mlsanity.com/paper/2311.01241/) (2023)
- [Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition](https://mlsanity.com/paper/2204.11434/) (2022)
- [Segmentation-free Direct Iris Localization Networks](https://mlsanity.com/paper/2210.10403/) (2022)

## Abstract (arXiv)

This paper proposes a deep feature extractor for iris recognition at arbitrary resolutions. Resolution degradation reduces the recognition performance of deep learning models trained by high-resolution images. Using various-resolution images for training can improve the model's robustness while sacrificing recognition performance for high-resolution images. To achieve higher recognition performance at various resolutions, we propose a method of resolution-adaptive feature extraction with automatically switching networks. Our framework includes resolution expert modules specialized for different resolution degradations, including down-sampling and out-of-focus blurring. The framework automatically switches them depending on the degradation condition of an input image. Lower-resolution experts are trained by knowledge-distillation from the high-resolution expert in such a manner that both experts can extract common identity features. We applied our framework to three conventional neural network models. The experimental results show that our method enhances the recognition performance at low-resolution in the conventional methods and also maintains their performance at high-resolution.