# Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images

arXiv: https://arxiv.org/abs/2304.11858 · HTML: https://mlsanity.com/paper/2304.11858/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation](https://mlsanity.com/paper/2010.09466/) (2020)
- [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)

## Built on by (most cited first, 1 of 1)

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

## Abstract (arXiv)

Fitness for Duty (FFD) techniques detects whether a subject is Fit to perform their work safely, which means no reduced alertness condition and security, or if they are Unfit, which means alertness condition reduced by sleepiness or consumption of alcohol and drugs. Human iris behaviour provides valuable information to predict FFD since pupil and iris movements are controlled by the central nervous system and are influenced by illumination, fatigue, alcohol, and drugs. This work aims to classify FFD using sequences of 8 iris images and to extract spatial and temporal information using Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM). Our results achieved a precision of 81.4% and 96.9% for the prediction of Fit and Unfit subjects, respectively. The results also show that it is possible to determine if a subject is under alcohol, drug, and sleepiness conditions. Sleepiness can be identified as the most difficult condition to be determined. This system opens a different insight into iris biometric applications.