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.

本研究通过使用卷积神经网络和长短时记忆网络，对8个虹膜图像序列进行分类，从虹膜行为中提取时空信息以精确预测人们是否适合开展工作，以及是否在使用酒精或药物，或者睡意影响下，结果显示可以用于识别这些条件以及最为难以识别的睡意条件。

使用虹膜及眼周图像时间序列进行在职适应性分类