# Towards an efficient Iris Recognition System on Embedded Devices

arXiv: https://arxiv.org/abs/2210.13101 · HTML: https://mlsanity.com/paper/2210.13101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 24 Oct 2022.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DenseNet: Implementing Efficient ConvNet Descriptor Pyramids](https://mlsanity.com/paper/1404.1869/) (2014)
- [Discriminative Correlation Filter with Channel and Spatial Reliability](https://mlsanity.com/paper/1611.08461/) (2016)
- [Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition](https://mlsanity.com/paper/1807.05248/) (2018)
- [CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation](https://mlsanity.com/paper/1901.01578/) (2019)
- [DeepIris: Iris Recognition Using A Deep Learning Approach](https://mlsanity.com/paper/1907.09380/) (2019)
- [SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression](https://mlsanity.com/paper/2003.00825/) (2020)
- [On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application](https://mlsanity.com/paper/2010.11700/) (2020)
- [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)
- [Selfie Periocular Verification using an Efficient Super-Resolution Approach](https://mlsanity.com/paper/2102.08449/) (2021)

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

- [Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images](https://mlsanity.com/paper/2304.11858/) (2023)
- [On the Feasibility of Creating Iris Periocular Morphed Images](https://mlsanity.com/paper/2408.13496/) (2024)

## Abstract (arXiv)

Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch with the process of capturing images with quality, calibrating operational distances, and building lightweight software such as eyes/iris detectors and segmentation sub-systems. In light of such challenges, this work aims to develop and implement iris recognition software in an embedding system and calibrate NIR in a contactless binocular setup. We evaluate and contrast speed versus performance obtained with two embedded computers and infrared cameras. Further, a lightweight segmenter sub-system called "Unet_xxs" is proposed, which can be used for iris semantic segmentation under restricted memory resources.