针对现有疲劳监测方法仅根据单帧图像嘴巴形态进行哈欠识别准确率低,采用阈值法分析眨眼参数适应性较差,无法对疲劳状态的过渡进行实时监测等问题,提出一种新的进行精神疲劳实时监测的多面部特征时序分类模型。首先,通过面部视觉特征提取张口度曲线与虹膜似圆比曲线;然后,采用滑动窗口分段、隐马尔可夫模型(HMM)建模等方法在张口度曲线的基础上构建哈欠特征时序并进行类别标记,在虹膜似圆比曲线的基础上构建眨眼持续时间时序并进行类别标记;最后,在HMM的基础上增加时间戳,以便自适应地选取时序初始时刻点并进行多个特征时序的同步与标记结果的融合。实验结果表明,本文模型可降低哈欠误判率,对不同年龄的人群眨眼具有很好的适应性,并可实现对精神疲劳过渡状态的实时监测。

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 α(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.