International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 5, October 2022, pp. 5493~5500 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp5493-5500  5493 Journal homepage: http://ijece.iaescore.com Deep convolutional network based real time fatigue detection and drowsiness alertness system Vijay Prakash Sharma 1 , Jitendra Singh Yadav 2 , Vivek Sharma 2 1 Department of Information Technology, Manipal University Jaipur, Jaipur, India 2 Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India Article Info ABSTRACT Article history: Received Jun 12, 2021 Revised May 27, 2022 Accepted Jun 25, 2022 Fatigue and drowsiness detection techniques based on the external features are under progress, and the methods of facial feature extraction require further development. This paper discusses the innovative processes, efficient methods, and recent advancements in the field of drowsiness and fatigue detection. In this proposed model, a wide application is planned in the field of artificial intelligence by defining the fundamentals of human-computer interaction, facial expression recognition and driver fatigue-sleepiness determination. This research outlines an efficient and effective three-phase strategy for detecting drowsiness. Viola Jones is used to detect facial traits in these three phases. Detection of yawning and tracking once the face has been identified, the segmenting the skin, the system becomes lighting invariant portion by itself, focusing on the chromatic components based on skin, and to reject most of non-face image backdrops. The color eye tracking and yawning detection are carried out by template matching with the correlation coefficient. The vectors of features based on each of the above phases is concatenated, and a binary result is obtained. The analysis of sound and successive frames into fatigue and non-fatigue states has been classified. If the time in fatigue state exceeds the threshold, the system will sound an alarm. Keywords: Artificial intelligence Convolutional neural network Fatigue detection Human computer interaction Machine learning This is an open access article under the CC BY-SA license. Corresponding Author: Vijay Prakash Sharma Department of Information Technology, Manipal University Jaipur Jaipur, India Email: vijayprakashsharma.muj@gmail.com 1. INTRODUCTION Nowadays, modern computing technologies have major advances in artificial intelligence. Brightness, blurring, individual variances in skin tone, and environmental variables all affect eye closure detection. It is a difficult process. Eye closure detection has various applications. Recently, driver assistance systems, smart car development and enhancement. Control and warning systems are examples. All artificial intelligence driverless vehicles are built in step with the information era. While research for driver assistance systems continues, solutions are generated based on need. Google, Manufacturers including Toyota, Nissan, BMW, and Tesla are continuing system R&D [1]. Currently available advanced driver-assistance systems (ADAS) Studies in numerous areas are seen when studied. The continual movement of road, car, and people generates life-threatening accidents [2]. Between 2009 and 2016, Turkey Statistics Institute (TSI) explained driver errors in road accidents as [3]. NHTSA (National Sleepiness is said to be the cause of 56,000 road fatalities and injuries per year [4]. Various studies to detect driver fatigue-sleepiness. These studies use body temperature, heart rate, and brain