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.

翻译：适岗状态（Fitness for Duty, FFD）检测技术用于判断受试者是否具备安全执行工作任务的能力，即无警觉性降低或安全隐患的"适岗"状态；或存在因困倦、酒精及药物摄入导致警觉性下降的"不适岗"状态。人类虹膜行为可为FFD预测提供重要信息，因为瞳孔和虹膜运动受中枢神经系统调控，并受光照、疲劳、酒精及药物等因素影响。本研究旨在通过8帧虹膜图像序列进行FFD分类，并运用卷积神经网络（CNN）与长短期记忆网络（LSTM）提取时空特征。实验结果显示，对适岗与不适岗受试者的预测准确率分别达到81.4%和96.9%。研究同时表明，该方法可有效识别受试者是否处于酒精影响、药物作用或困倦状态，其中困倦状态被证实为最难判定的条件。该系统为虹膜生物特征应用开辟了新的研究方向。