基于聚类框架与局部感受野的实时人脸疲劳检测  

Real-time face fatigue detection based on receptive field and clustering

作　　者：刘君扬 王金凤[1] Liu Junyang;Wang Jinfeng(College of Mathematics&Informatics,South China Agricultural University,Guangzhou 510642,China)

机构地区：[1]华南农业大学数学与信息学院,广州510642

出　　处：《计算机应用研究》2020年第12期3795-3798,共4页Application Research of Computers

基　　金：广东省科技计划资助项目(2017A040406023);广州市科技计划资助项目(201804010353)。

摘　　要：针对在自然环境下人脸疲劳识别遇到的问题,如人脸检测率不高、判别疲劳的特征过于单一、检测速度慢等,提出了一种基于聚类框架与局部感受野的实时人脸疲劳检测方法。首先对人脸尺寸进行聚类分析,根据聚类类别决定检测层个数并设置先验框大小,根据预测特征图的感受野与人脸尺寸匹配的原则设置网络层数,最后通过最小化损失函数学习多种疲劳特征。实验证明,在驾驶室等环境下基于聚类框架与局部感受野的方法在保持识别准确率的同时提高了检测速度,使用GPU GeForce GTX TITAN能达到125 fps,满足了实时性要求。Faced with the problems encountered in fatigue detection under natural environment,such as low detection rate of the face,slow detection speed and single feature for judging,etc.,this paper proposed a fatigue detection method combining receptive field with clustering algorithm.Firstly,it applied cluster on the face size and returned the cluster numbers which determined the number of detection layer.Then,it set the size of anchor boxes according to the face size.Secondly,the algorithm set the number of convolutional layers according to the principle that the receptive field should match the face size in the predicted feature map.Finally,new algorithm learnt a variety of fatigue characteristics by minimizing the loss function.Experiments show that this method based on clustering framework and local receptive field have improved the detection speed while maintaining the recognition accuracy.It can reach 125 fps by using GPU GeForce GTX TITAN,and satisfy the request of real time.

关 键 词：神经网络 深度学习 目标检测 疲劳识别 感受野 聚类 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]