  This paper focuses on the challenge of driver safety on the road and presents
a novel system for driver drowsiness detection. In this system, to detect the
falling sleep state of the driver as the sign of drowsiness, Convolutional
Neural Networks (CNN) are used with regarding the two goals of real-time
application, including high accuracy and fastness. Three networks introduced as
a potential network for eye status classifcation in which one of them is a
Fully Designed Neural Network (FD-NN) and others use Transfer Learning in VGG16
and VGG19 with extra designed layers (TL-VGG). Lack of an available and
accurate eye dataset strongly feels in the area of eye closure detection.
Therefore, a new comprehensive dataset proposed. The experimental results show
the high accuracy and low computational complexity of the eye closure
estimation and the ability of the proposed framework on drowsiness detection.
