计算机与数字工程2025，Vol.53Issue(3)：821-828,8.DOI:10.3969/j.issn.1672-9722.2025.03.035
基于面部动作和头部姿态疲劳检测方法
Fatigue Detection Method Based on Facial Movement and Head Posture
摘要
Abstract
Fatigue driving is one of the major causes of modern traffic accidents.Aiming at the problems of single fatigue fea-ture,low robustness and difficulty in extracting eye fatigue feature in current fatigue detection methods,visual information from two dimensions of facial movement and head posture is combined to detect fatigue.Firstly,MTCNN+PFLD deep network is used to de-tect the face and locate 68 facial feature points.For facial movements,after eye matting and data enhancement are preprocessed for face images,deep residual network is established to extract eye movement signals by cascade classification.Meanwhile,aspect ratio is calculated according to the information of feature points.By setting a reasonable threshold value to detect the mouth movement.For the head pose,solvePnP algorithm combined with the information of feature points in the two-dimensional space is used to trans-form the coordinate system space to solve the Euler Angle of the head pose.Finally,according to the P80 principle of Perclos algo-rithm and the physiological reality,the detection results of single frame fatigue feature are extracted and then multi-frame compre-hensive voting is conducted to further improve the robustness.The experimental results show that the average processing time of sin-gle frame image is 49.3 ms,and the detection accuracy is more than 97.8%in different scenes.The model generalization ability is strong,and it has high practical value and practical significance.关键词
疲劳驾驶/深度学习/MTCNN/PFLD/solvePnP/多帧综合投票/PerclosKey words
fatigue driving/deep learning/MTCNN/PFLD/solvePnP/multi-frame comprehensive voting/Perclos分类
信息技术与安全科学引用本文复制引用
刘子恒,焦良葆,孟琳,孙宏伟,魏小玉..基于面部动作和头部姿态疲劳检测方法[J].计算机与数字工程,2025,53(3):821-828,8.基金项目
国家自然科学基金青年基金项目(编号:61903183)资助. （编号:61903183）
