# Iris Recognition Based on SIFT Features

arXiv: https://arxiv.org/abs/2111.00176 · HTML: https://mlsanity.com/paper/2111.00176/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 19 references that are in the map.
- First posted on arXiv 30 Oct 2021.

## Built on by (most cited first, 11 of 11)

- [Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition](https://mlsanity.com/paper/2208.03138/) (2022)
- [Iris super-resolution using CNNs: is photo-realism important to iris recognition?](https://mlsanity.com/paper/2210.13125/) (2022)
- [Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition](https://mlsanity.com/paper/2407.19472/) (2024)
- [Exploring Deep Learning Image Super-Resolution for Iris Recognition](https://mlsanity.com/paper/2311.01241/) (2023)
- [Compact multi-scale periocular recognition using SAFE features](https://mlsanity.com/paper/2210.09778/) (2022)
- [Experimental analysis regarding the influence of iris segmentation on the recognition rate](https://mlsanity.com/paper/2211.05507/) (2022)
- [A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning](https://mlsanity.com/paper/2203.14203/) (2022)
- [Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion](https://mlsanity.com/paper/2210.09765/) (2022)
- [Super-Resolution and Image Re-projection for Iris Recognition](https://mlsanity.com/paper/2210.11129/) (2022)
- [Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition](https://mlsanity.com/paper/2311.01237/) (2023)
- [Keypoint Description by Symmetry Assessment -- Applications in Biometrics](https://mlsanity.com/paper/2311.01651/) (2023)

## Abstract (arXiv)

Biometric methods based on iris images are believed to allow very high accuracy, and there has been an explosion of interest in iris biometrics in recent years. In this paper, we use the Scale Invariant Feature Transformation (SIFT) for recognition using iris images. Contrarily to traditional iris recognition systems, the SIFT approach does not rely on the transformation of the iris pattern to polar coordinates or on highly accurate segmentation, allowing less constrained image acquisition conditions. We extract characteristic SIFT feature points in scale space and perform matching based on the texture information around the feature points using the SIFT operator. Experiments are done using the BioSec multimodal database, which includes 3,200 iris images from 200 individuals acquired in two different sessions. We contribute with the analysis of the influence of different SIFT parameters on the recognition performance. We also show the complementarity between the SIFT approach and a popular matching approach based on transformation to polar coordinates and Log-Gabor wavelets. The combination of the two approaches achieves significantly better performance than either of the individual schemes, with a performance improvement of 24% in the Equal Error Rate.