Driver fatigue detection is a significant application in smart cars. In order to improve the accuracy and timeliness of driver fatigue detection, a fatigue detection algorithm based on deeply-learned facial expression analysis is proposed. Specifically, the face key point detection model is first trained by multi block local binary patterns (MB-LBP) and Adaboost classifier. Subsequently, the eyes and mouth state are detected by using the trained model to detect the 24 facial features. Afterwards, we calculate the number of two parameters that can describe the driver's fatigue state and the proportion of the closed eye time within the unit time (PERCLOS) and yawning frequency. Finally, the fuzzy inference system is utilized to deduce the driver's fatigue state (normal, slight fatigue, severe fatigue). Experimental results show that the proposed algorithm can detect driver fatigue degree quickly and accurately.

驾驶员疲劳检测是智能汽车中的重要应用。为了提高驾驶员疲劳检测的准确性和及时性，提出了一种基于深度学习的面部表情分析的疲劳检测算法。具体而言，首先通过多块局部二进制模式（MB-LBP）和Adaboost分类器训练面部关键点检测模型。随后，通过使用经过训练的模型来检测24个面部特征，以检测眼睛和嘴巴的状态。然后，我们计算两个可以描述驾驶员疲劳状态的参数的数量，以及在单位时间（PERCLOS）和打哈欠频率下闭眼时间的比例。最后，利用模糊推理系统推断驾驶员的疲劳状态（正常，轻度疲劳，严重疲劳）。