# Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects

arXiv: https://arxiv.org/abs/2106.15828 · HTML: https://mlsanity.com/paper/2106.15828/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [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)
- [Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks](https://mlsanity.com/paper/1809.00769/) (2018)

## Built on by (most cited first, 6 of 6)

- [Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences](https://mlsanity.com/paper/2203.02488/) (2022)
- [Alcohol Consumption Detection from Periocular NIR Images Using Capsule Network](https://mlsanity.com/paper/2209.01657/) (2022)
- [Towards an efficient Iris Recognition System on Embedded Devices](https://mlsanity.com/paper/2210.13101/) (2022)
- [Learning to Predict Fitness for Duty using Near Infrared Periocular Iris Images](https://mlsanity.com/paper/2209.01683/) (2022)
- [Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation](https://mlsanity.com/paper/2212.12733/) (2022)
- [On the Feasibility of Creating Iris Periocular Morphed Images](https://mlsanity.com/paper/2408.13496/) (2024)

## Abstract (arXiv)

This paper proposes a new framework to detect, segment, and estimate the localization of the eyes from a periocular Near-Infra-Red iris image under alcohol consumption. The purpose of the system is to measure the fitness for duty. Fitness systems allow us to determine whether a person is physically or psychologically able to perform their tasks. Our framework is based on an object detector trained from scratch to detect both eyes from a single image. Then, two efficient networks were used for semantic segmentation; a Criss-Cross attention network and DenseNet10, with only 122,514 and 210,732 parameters, respectively. These networks can find the pupil, iris, and sclera. In the end, the binary output eye mask is used for pupil and iris diameter estimation with high precision. Five state-of-the-art algorithms were used for this purpose. A mixed proposal reached the best results. A second contribution is establishing an alcohol behavior curve to detect the alcohol presence utilizing a stream of images captured from an iris instance. Also, a manually labeled database with more than 20k images was created. Our best method obtains a mean Intersection-over-Union of 94.54% with DenseNet10 with only 210,732 parameters and an error of only 1-pixel on average.