CNN Based Driver Drowsiness Detection System Using Emotion Analysis H. Varun Chand * and J. Karthikeyan School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India  Corresponding Author: H. Varun Chand. Email: varunchand.h2016@vitstudent.ac.in Received: 05 May 2021; Accepted: 16 June 2021 Abstract: The drowsiness of the driver and rash driving are the major causes of road accidents, which result in loss of valuable life, and deteriorate the safety in the road traffic. Reliable and precise driver drowsiness systems are required to prevent road accidents and to improve road traffic safety. Various driver drowsi- ness detection systems have been designed with different technologies which have an affinity towards the unique parameter of detecting the drowsiness of the driver. This paper proposes a novel model of multi-level distribution of detecting the dri- ver drowsiness using the Convolution Neural Networks (CNN) followed by the emotion analysis. The emotion analysis, in this proposed model, analyzes the dri- ver ’ s frame of mind which identifies the motivating factors for different driving patterns. These driving patterns were analyzed based on the acceleration system, speed of the vehicle, Revolutions per Minute (RPM), facial recognition of the dri- ver. The facial pattern of the driver is treated with 2D Convolution Neural Net- work (CNN) to detect the behavior and driver ’ s emotion. The proposed model is implemented using OpenCV and the experimental results prove that the pro- posed model detects the driver ’ s emotion and drowsiness more effectively than the existing technologies. Keywords: Driver drowsiness; emotion analysis; convolution neural network; driver fatigue; driver mentality 1 Introduction The increase in population and the usage of the automobile has increased the negative outcomes of road accidents, deadly injuries, loss of valuable life, financial losses, and non-recoverable health and mental illness. The National Crime Records Bureau (NCRB) has released a report during the year 2020 on the statistical analysis of road accidents [1]. The report states that there are around 5 Lakhs of road accidents which have been reported in one year, among which 69% creates a high level of damage to life and property. The report extends to the analysis of factors influencing road accidents. Driver drowsiness and mentality are the vital factors for road accidents and rash driving [2]. The drowsiness of the driver may be due to restless driving, fatigue, consumption of alcohol while the mentality relates to extreme anger, frustration, and sometimes extreme happiness. Based on the analysis report, the driver ’ s behavior is the vital cause for road accidents, which motivates a lot of researchers to be involved in monitoring and detecting the driver ’ s drowsiness systems. Some notable research results were implemented in real-time to This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.020008 Article ech T Press Science