  Road crashes and related forms of accidents are a common cause of injury and
death among the human population. According to 2015 data from the World Health
Organization, road traffic injuries resulted in approximately 1.25 million
deaths worldwide, i.e. approximately every 25 seconds an individual will
experience a fatal crash. While the cost of traffic accidents in Europe is
estimated at around 160 billion Euros, driver drowsiness accounts for
approximately 100,000 accidents per year in the United States alone as reported
by The American National Highway Traffic Safety Administration (NHTSA). In this
paper, a novel approach towards real-time drowsiness detection is proposed.
This approach is based on a deep learning method that can be implemented on
Android applications with high accuracy. The main contribution of this work is
the compression of heavy baseline model to a lightweight model. Moreover,
minimal network structure is designed based on facial landmark key point
detection to recognize whether the driver is drowsy. The proposed model is able
to achieve an accuracy of more than 80%. Keywords: Driver Monitoring System;
Drowsiness Detection; Deep Learning; Real-time Deep Neural Network; Android.
