# Iris-SAM: Iris Segmentation Using a Foundation Model

arXiv: https://arxiv.org/abs/2402.06497 · HTML: https://mlsanity.com/paper/2402.06497/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [The ND-IRIS-0405 Iris Image Dataset](https://mlsanity.com/paper/1606.04853/) (2016)
- [Near-infrared and visible-light periocular recognition with Gabor features using frequency-adaptive automatic eye detection](https://mlsanity.com/paper/2211.05544/) (2022)
- [Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks](https://mlsanity.com/paper/1901.01028/) (2019)

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

- [FRoundation: Are Foundation Models Ready for Face Recognition?](https://mlsanity.com/paper/2410.23831/) (2024)
- [FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection](https://mlsanity.com/paper/2501.02892/) (2025)
- [MADation: Face Morphing Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.03800/) (2025)
- [Benchmarking Foundation Models for Zero-Shot Biometric Tasks](https://mlsanity.com/paper/2505.24214/) (2025)
- [Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition](https://mlsanity.com/paper/2507.03541/) (2025)
- [Towards Iris Presentation Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.06312/) (2025)
- [Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?](https://mlsanity.com/paper/2506.05263/) (2025)
- [Can Foundation Models Predict Fitness for Duty?](https://mlsanity.com/paper/2507.20418/) (2025)

## Abstract (arXiv)

Iris segmentation is a critical component of an iris biometric system and it involves extracting the annular iris region from an ocular image. In this work, we develop a pixel-level iris segmentation model from a foundational model, viz., Segment Anything Model (SAM), that has been successfully used for segmenting arbitrary objects. The primary contribution of this work lies in the integration of different loss functions during the fine-tuning of SAM on ocular images. In particular, the importance of Focal Loss is borne out in the fine-tuning process since it strategically addresses the class imbalance problem (i.e., iris versus non-iris pixels). Experiments on ND-IRIS-0405, CASIA-Iris-Interval-v3, and IIT-Delhi-Iris datasets convey the efficacy of the trained model for the task of iris segmentation. For instance, on the ND-IRIS-0405 dataset, an average segmentation accuracy of 99.58% was achieved, compared to the best baseline performance of 89.75%.