# On the Feasibility of Creating Iris Periocular Morphed Images

arXiv: https://arxiv.org/abs/2408.13496 · HTML: https://mlsanity.com/paper/2408.13496/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [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)
- [DeepIris: Iris Recognition Using A Deep Learning Approach](https://mlsanity.com/paper/1907.09380/) (2019)
- [Face Feature Visualisation of Single Morphing Attack Detection](https://mlsanity.com/paper/2304.13021/) (2023)
- [Open Source Iris Recognition Hardware and Software with Presentation Attack Detection](https://mlsanity.com/paper/2008.08220/) (2020)
- [Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects](https://mlsanity.com/paper/2106.15828/) (2021)
- [Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features](https://mlsanity.com/paper/1905.00372/) (2019)
- [Updatable Siamese Tracker with Two-stage One-shot Learning](https://mlsanity.com/paper/2104.15049/) (2021)
- [Towards an efficient Iris Recognition System on Embedded Devices](https://mlsanity.com/paper/2210.13101/) (2022)

## Abstract (arXiv)

In the last few years, face morphing has been shown to be a complex challenge for Face Recognition Systems (FRS). Thus, the evaluation of other biometric modalities such as fingerprint, iris, and others must be explored and evaluated to enhance biometric systems. This work proposes an end-to-end framework to produce iris morphs at the image level, creating morphs from Periocular iris images. This framework considers different stages such as pair subject selection, segmentation, morph creation, and a new iris recognition system. In order to create realistic morphed images, two approaches for subject selection are explored: random selection and similar radius size selection. A vulnerability analysis and a Single Morphing Attack Detection algorithm were also explored. The results show that this approach obtained very realistic images that can confuse conventional iris recognition systems.