Real time Driver’s Drowsiness Detection by Convolution Neural Network (CNN) of Deep Learning Approach

Abstract

Statistics have shown that 20% of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This work will propose real-time drowsiness detection; this approach is based on Convolution Neural Network (CNN) of Deep Learning. Which is aimed to implement driver’s behavior-based drowsiness detection scenario. Convolution Neural Network (CNN) for learning effective features or facial landmark input to detecting drowsiness by given an input video of driver. A common global face which is not capable enough to extracting effective facial landmarks and features, like facial movements and head gestures, which are strictly important for learning. This proposed work consists Convolution Neural Network (CNN) for attaining well-aligned facial movements and head gestures important for reliable detection. The output of neural network is integrated and feed to classifier for drowsiness detection.