Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 227-229.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Yawning Detection Algorithm Based on Convolutional Neural Network

MA Su-gang1,2,ZHAO Chen2,SUN Han-lin2,HAN Jun-gang2   

  1. School of Information Engineering,Chang’an University,Xi’an 710064,China1
    School of Computer Science and Technology,Xi’an University of Posts and Telecommunications,Xi’an 710121,China2
  • Online:2018-06-20 Published:2018-08-03

Abstract: Yawning detection can be used to warn drivers of fatigue driving behavior,thereby reducing traffic accidents.A yawning detection algorithm based on convolutional neural network was proposed.The driver’s facial image can be directly used as input for neural network,so as to avoid the complex explicit feature extraction of the facial image.The Softmax classifier is used to classify the features extracted from the neural network to determine whether the behavior is yawning or not.This algorithm achieves 92.4% accuracy in the YawDD dataset.Compared with other existing algorithms,the proposed method has the advantages of high detection accuracy and simpleimplementation.

Key words: Yawning detection, Convolutional neural network, Weight sharing, Softmax classifier, Yawning detection dataset

CLC Number: 

  • TP183
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