Classify NIR Iris Images Under Alcohol/Drugs/Sleepiness Conditions Using a Siamese Network

This paper proposes a biometric application for iris capture devices using a Siamese network based on an EfficientNetv2 and a triplet loss function to classify iris NIR images captured under alcohol/drugs/sleepiness conditions. The results show that our model can detect the “Fit/Unfit” alertness condition from iris samples captured after alcohol, drug consumption, and sleepiness conditions robustly with an accuracy of 87.3% and 97.0% for Fit/Unfit, respectively. The sleepiness condition is the most challenging, with an accuracy of 72.4%. The Siamese model uses a smaller number of parameters than the standard Deep learning Network algorithm. This work complements and improves the literature on biometric applications for developing an automatic system to classify “Fitness for Duty” using iris images and prevent accidents due to alcohol/drug consumption and sleepiness.