Balarama Krishna Padamata, et. al. International Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 10, Issue 11, (Series-I) November 2020, pp. 58-65 www.ijera.com DOI: 10.9790/9622-1011015865 58 | Page A Machine Learning Approach for Driver Drowsiness Detection 1 Balarama Krishna Padamata, 2 Dr. Jhansi Rani Singothu 1 M.tech in CST With Artificial Intelligence And Robotics, Department of Computer Science and Systems Engineering, Andhra University College Of Engineering, Andhra University, Visakhapatnam, AP, India 2 Assistant Professor, Department of Computer Science and Systems Engineering, Andhra University College Of Engineering, Andhra University, Visakhapatnam, AP, India. ABSTRACT: Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. The proposed algorithm makes use of features learnt using convolutional neural network so as to explicitly capture various latent facial features and the complex non-linear feature interactions. A softmax layer is used to classify the driver as drowsy or non-drowsy. This system is hence used for warning the driver of drowsiness or in attention to prevent traffic accidents. We present both qualitative and quantitative results to substantiate the claims made in the paper. KEYWORDS: Driver Drowsiness, Artificial Intelligence, Feature learning, deep learning, Convolutional Neural Networks --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 31-10-2020 Date of Acceptance: 12-11-2020 --------------------------------------------------------------------------------------------------------------------------------------- I. INTRODUCTION In a car safety technology, driver drowsiness detection [1-3] is very essential to prevent road accidents. Now-a-days, many people using automobiles for daily commutation, higher living standards, comfortability, and timing constraints to reach destinations. This trend leads to high volumes of traffic in urban areas and highways. In turn, it will raise number of road accidents with several factors. Driver drowsiness could be the one reason for road accidents. One way to reduce number of accidents is early detection of driver drowsiness and alerting with an alarm. According to the NHTSA, every year around 1 lakh road accidents occurs because of driver drowsiness in the United States. NHTSA reported that 72,000 road accidents, 800 deaths and 44,000 injuries are occurred due to driver drowsiness. In 2017, around 1.47 lakh people are died due road accidents in India. Every year, over a Lakh people lost life due to road crashes and more than 4 times people get injured due to road accidents. In India average road accidents deaths are 1, 36,118 per year in last one decade. In 2016, 60% of people who lost their lives in road accidents were in age group of between in 18-35. In India, since 2012 more than 500 people died due accidents on Yamuna express way and more than 100 people died due to vehicle crashes on Agra-Lucknow express way. Police officials and patrolling teams on these expressways revealed that most of the accidents are happened between 2 am and 5 am due to drivers drowsy-deprived. Drivers’ sleep deprivation is major reason for accidents. So, technology for driver drowsiness detection system is required to reduce road accidents. The development of this technology is a big challenge for both an industrial and research community. There are different signs of driver drowsiness can be observed while driving the vehicle such as in ability to keep eyes open, frequently yawning, moving the head forward etc. To determine the level of driver drowsiness various measures are used. These measures are RESEARCH ARTICLE OPEN ACCESS