We have performed a direct comparison between facial features obtained from a webcam and vigilance-task performance during prolonged wakefulness. Prolonged wakefulness deteriorates working performance due to changes in cognition, emotion, and by delayed response. Facial features can be potentially collected everywhere using webcams located in the workplace. If this type of device can obtain relevant information to predict performance deterioration, this technology can potentially reduce serious accidents and fatality. We extracted 34 facial indices, including head movements, facial expressions, and perceived facial emotions from 20 participants undergoing the psychomotor vigilance task (PVT) over 25 hours. We studied the correlation between facial indices and the performance indices derived from PVT, and evaluated the feasibility of facial indices as detectors of diminished reaction time during the PVT. Furthermore, we tested the feasibility of classifying performance as normal or impaired using several machine learning algorithms with correlated facial indices. Twenty-one indices were found significantly correlated with PVT indices. Pitch, from the head movement indices, and four perceived facial emotions—anger, surprise, sadness, and disgust—exhibited significant correlations with indices of performance. The eye-related facial expression indices showed especially strong correlation and higher feasibility of facial indices as classifiers. Significantly correlated indices were shown to explain more variance than the other indices for most of the classifiers. The facial indices obtained from a webcam strongly correlate with working performance during 25 hours of prolonged wakefulness.

我们已经对从网络摄像头获得的面部特征与长时间清醒期间的警觉任务表现进行了直接比较。长时间的清醒会由于认知，情绪变化和反应迟缓而降低工作效率。可以使用工作场所中的网络摄像头在任何地方收集面部特征。如果此类设备可以获取相关信息以预测性能下降，则该技术可以潜在地减少严重事故和死亡人数。我们从25名接受了心理运动警戒任务（PVT）的20名参与者中提取了34种面部指数，包括头部运动，面部表情和感知的面部表情。我们研究了面部指标与PVT得出的性能指标之间的相关性，并评估了面部指标作为PVT期间反应时间缩短的检测器的可行性。此外，我们测试了使用具有相关面部指数的几种机器学习算法将性能分类为正常或受损的可行性。发现21个指数与PVT指数显着相关。从头部运动指数来看，音调和四种感知到的面部情绪（愤怒，惊奇，悲伤和厌恶）与表现指数之间具有显着相关性。与眼睛相关的面部表情指数显示出特别强的相关性，并且面部指数作为分类器的可行性更高。对于大多数分类器而言，显示出显着相关的指数比其他指数能解释更多的方差。