# Alcohol Consumption Detection from Periocular NIR Images Using Capsule Network

arXiv: https://arxiv.org/abs/2209.01657 · HTML: https://mlsanity.com/paper/2209.01657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 4 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Deep Face Representations for Differential Morphing Attack Detection](https://mlsanity.com/paper/2001.01202/) (2020)
- [Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects](https://mlsanity.com/paper/2106.15828/) (2021)
- [Relevant features for Gender Classification in NIR Periocular Images](https://mlsanity.com/paper/1904.12007/) (2019)
- [Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences](https://mlsanity.com/paper/2203.02488/) (2022)

## 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)
- [Can Foundation Models Predict Fitness for Duty?](https://mlsanity.com/paper/2507.20418/) (2025)

## Abstract (arXiv)

This research proposes a method to detect alcohol consumption from Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors such as alcohol on the Central Nervous System (CNS). The goal is to analyse how this impacts on iris and pupil movements and if it is possible to capture these changes with a standard iris NIR camera. This paper proposes a novel Fused Capsule Network (F-CapsNet) to classify iris NIR images taken under alcohol consumption subjects. The results show the F-CapsNet algorithm can detect alcohol consumption in iris NIR images with an accuracy of 92.3% using half of the parameters as the standard Capsule Network algorithm. This work is a step forward in developing an automatic system to estimate "Fitness for Duty" and prevent accidents due to alcohol consumption.