基于聚类框架与局部感受野的实时人脸疲劳检测.

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. [ABSTRACT FROM AUTHOR]