Real time detection system of driver drowsiness based on representation learning using deep neural networks

One of the major issues of road accidents all over the world is drowsiness state of the driver. It is a complex phenomenon to measure a driver’s consciousness in a direct manner. This work proposes with three deep neural architecture for learning facial features which consists of 68 attributes from...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2019-01, Vol.36 (3), p.1977-1985
Main Authors: Vijayan, Vineetha, Sherly, Elizabeth
Format: Article
Language:English
Subjects:
Accidents
Architecture
Artificial neural networks
Blinking
Driver fatigue
Experimentation
Learning
Neural networks
Representations
Yawning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:One of the major issues of road accidents all over the world is drowsiness state of the driver. It is a complex phenomenon to measure a driver’s consciousness in a direct manner. This work proposes with three deep neural architecture for learning facial features which consists of 68 attributes from the RGB video input of a driver. The experimentation is conducted by three different CNN models such as ResNet50, VGG16 and InceptionV3. These three networks are combined for representation learning which then put together the features to form a feature fused architecture(FFA). The trained features as well as facial movements such as eye blinking, yawning and head swaying are again trained with a softmax classifier to classify the drowsiness state of driver. Out of the three networks and FFA, InceptionV3 shows 78% accuracy.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-169909