In computer vision based fatigue monitoring

there are still some unresolved issues remained

including low re-cognition accuracy in yawn detection based on a single-frame; poor adaptability in blink analysis because of the required threshold

the inability to monitor the transition stages of fatigue in real-time. Attempted to solve these problems

we propose a new classification model in this paper

which is based on two feature time-series for real-time mental fatigue monitoring. First

the mouth opening degree and iris circularity ratio are calculated through facial visual feature extraction. Based on this

we can generate a corresponding time-series called (the proportion of the time during which mouth opening exceeds a given threshold)time series and eye blink time (EBT) time series. Then

using sliding window to partition and annotate the two kinds of time series and build hidden markov model (HMM) for EBT time series. Finally

add a time stamp on HMM to adaptively calculate the initial time point of the next time series

in addition

we can use it to perform the synchronization and fusion of the two time series. Experimental results show that the promoted model can improve yawn detection rate

have good adaptability for blink features of different age groups

and can monitor the transition stage of mental fatigue in real-time.