Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images
This addresses safety monitoring in workplaces by providing a non-invasive method to assess alertness, though it is incremental as it applies existing CNN and LSTM techniques to a new application domain.
The paper tackles the problem of classifying Fitness-for-Duty (FFD) by detecting reduced alertness due to sleepiness, alcohol, or drugs using sequences of iris images, achieving precisions of 81.4% for Fit subjects and 96.9% for Unfit subjects.
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.