  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.
