# Experimental analysis regarding the influence of iris segmentation on the recognition rate

arXiv: https://arxiv.org/abs/2211.05507 · HTML: https://mlsanity.com/paper/2211.05507/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [Iris Recognition Based on SIFT Features](https://mlsanity.com/paper/2111.00176/) (2021)

## Built on by (most cited first, 2 of 2)

- [Post-Mortem Human Iris Segmentation Analysis with Deep Learning](https://mlsanity.com/paper/2408.03448/) (2024)
- [Explicit Over Implicit: Enhancing CNNs Via Complex Structure Tensor Representations for Periocular Recognition](https://mlsanity.com/paper/2607.15410/) (2026)

## Abstract (arXiv)

In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance.