# Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

arXiv: https://arxiv.org/abs/2203.02488 · HTML: https://mlsanity.com/paper/2203.02488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 73 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects](https://mlsanity.com/paper/2106.15828/) (2021)
- [Fatigue Assessment using ECG and Actigraphy Sensors](https://mlsanity.com/paper/2008.02871/) (2020)

## Built on by (most cited first, 3 of 3)

- [Alcohol Consumption Detection from Periocular NIR Images Using Capsule Network](https://mlsanity.com/paper/2209.01657/) (2022)
- [Learning to Predict Fitness for Duty using Near Infrared Periocular Iris Images](https://mlsanity.com/paper/2209.01683/) (2022)
- [Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images](https://mlsanity.com/paper/2304.11858/) (2023)

## Abstract (arXiv)

This paper proposes a new method to estimate behavioural curves from a stream of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness For Duty system (FFD). The research focuses on determining the effect of external factors such as alcohol, drugs, and sleepiness on the Central Nervous System (CNS). The aim is to analyse how this behaviour is represented on iris and pupil movements and if it is possible to capture these changes with a standard NIR camera. The behaviour analysis showed essential differences in pupil and iris behaviour to classify the workers in "Fit" or "Unfit" conditions. The best results can distinguish subjects robustly under alcohol, drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient Boosted Machine reached the best results in all groups with an overall accuracy for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open a new application for iris capture devices.