Real-Time Driver Drowsiness Detection System Using Dlib based on Driver Eye/Mouth Monitoring Technology

Abstract

The driver sleepiness is common and one of the main causes of road accidents. So, there is a need for automatically detecting of this human behaviour. In any case, if the drivers feel drowsy, they still keep on driving the vehicle, and accidents occur. This study can be implemented using the CNN training model and initiating an alarm if the drowsiness condition is detected. Many of the authors suggested the process of detecting the Drowsiness (Problem Statement) of the drivers using technologies like the Internet of Things (IoT), Deep learning, and Haar cascade (to detect the coordinates of eyes and mouth, which are the target objects). However, this study contributes towards providing the real-time application in co-operating the CNN model and Dlib. Hence, this study proposes a novel embedded system with CNN technology. The CNN model is fed with inputs based on four (4) images related to eye and mouth openings and closings. This application is trained using the CNN model, which takes inputs as images and processes by identifying the features on the face using the Dlib library while representing the change in the state of coordinates of eyes and mouth as Yawning. This approach is achieved using Convolution Neural Network (CNN), pillow, Pygame, OpenCV, and the Dlib, along with providing an alarm when the position of mouth changes. The model is recorded with a maximum validation accuracy of 98% with the minimum recorded loss of less than 0.04% as areal-time application.