Lei Zhao This email address is being protected from spambots. You need JavaScript enabled to view it.1, Guoxin Zhang2

1School of Mechanical and Electronic Engineering, Shandong Jianzhu University, No.1000 of Fengming Road, Jinan, China
2Department of Mechanical Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong


Received: August 7, 2019
Accepted: March 26, 2020
Publication Date: September 1, 2020
Download Citation: ||https://doi.org/10.6180/jase.202009_23(3).0001  


A novel approach for the detecting dynamic facial fatigue behavior of drivers is proposed in this work. First, the eye and mouth regions are extracted from a frame in a video to classify the static facial fatigue state by using a local ensemble convolutional neural network (LECNN) model. A transfer learning strategy is used to pretrain the LECNN model for improved recognition accuracy. Second, the classification results for the frames in the video are encoded and connected to construct an encoding vector that represents the facial state in the video. Finally, k-nearest neighbors classifier is adopted to classify the constructed vector and obtain the recognition result of facial fatigue. The proposed system is tested on a human fatigue dataset of images that are captured in different environments. Results demonstrate the stable and robust performance of the proposed method with approximately 96.76% accuracy.

Keywords: Driver fatigue recognition, Local ensemble convolutional neural networks, Transfer learning.


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