Driving Alert System Based on Facial Expression Recognition

Abstract

Facial expression recognition is one of the most significant aspect of our life at all levels. Realizing the impact of this feature to our society, this project hence developed a driver’s fatigue and drowsy detection system to detect a fatigue driver while driving a car. Present trends had suggested that driving and navigation support systems are getting much importance today as it has become a crucial element to support drivers in various aspects within the automobile industry. Hence, this system is important for driving support systems to detect the status or activity of driver’s consciousness. The methodology development that used in this system is Waterfall Methodology model, where each phase is important to achieving the goals of the project. Each phase in this Waterfall model is important for reaching the requirement of clients and accomplish the goal of the project. Basically, this system assists the driver and calculate the state of behaviour according to the driver face. Moreover, once the measurement process has been carried out by OpenCV Python, this application would instantly provide immediate alert sound to the driver through this application. Anaconda is an environment to create and implement an algorithm to detect yawn and drowsy. The system has been tested and managed to detect fatigue and drowsy then sent alert to driver. Thus, hopefully this system can prevent and reduce the number of road accidents caused by sleepy drivers. In addition, this system might save countless lives in Malaysia. Technology Acceptance Model (TAM) was being used to gather feedback in a form of questionnaires that were distributed online, where maximum number of 87 responses were successfully gathered.