International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3929 A Review Paper on Visual Analysis of Eye State using Image Processing for Driver Fatigue Detection Miss. Ankita Bhoyar 1 , Prof. S. N. Sawalkar 2 1 Student, Computer Science and Engineering, Sipna C.O.E.T., Maharashtra, India 2 Assistant professor, Computer Science and Engineering, Sipna C.O.E.T., Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Driver’s fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in working conditions. In this , propose a vision- based fatigue detection system for driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules image acquisition , image resize, Haar Cascadec Classifier, dlib facial landmark detector, 68 landmark, eye region, eye region of interest, eye aspect ratio (EAR). A robust measure of eye aspect ratio (EAR) on the continuous level of eye openness is defined, and the driver states are classified on it. In experiments, systematic evaluations and analysis of proposed algorithms, as well as comparison with ground truth on EAR measurements, are performed. The experimental results show the advantages of the system on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver’s face is used for driving state monitoring. Key Words: Haar cascade classifier, dlib face detector , eye aspect ratio (EAR), openCV, fatigue detection. 1. INTRODUCTION Fatigue, drowsiness and sleepiness are often used synonymously in driving state description. Involving multiple human factors, it is multidimensional in nature that researchers have found difficult to define over past decades. Despite the ambiguity surrounding fatigue, it is a critical factor for driving safety. Studies have shown that fatigue is one of the leading contributing factors in traffic accidents worldwide. It is particularly critical for occupational drivers, such as drivers of buses and heavy trucks, due to the fact that they may have to work over a prolonged duration of the driving task, during the peak drowsiness periods (i.e., 2:00 A.M. to 6:00 A.M. and 2:00 P.M. to 4:00 P.M.), and under monotonous or boredom working conditions. Drowsy driving is becoming one of the most important cause of road accidents. According to many surveys around 30% of road accidents is due to the driver fatigue and the percentage is increasing every year. Drowsiness can be due to the adverse driving conditions, heavy traffic, workloads, late night long drive etc. Lack of sleep, absence of rest, taking medicines are also causes for drowsiness. When driver drives for more than the normal period fatigue is caused and the driver may feel tiredness which will cause driver to sleepy condition and loss of consciousness. This results road accidents and death of driver or serious injuries and also claims thousands of lives every year. Drowsiness is a phenomenon which is the transition period from the awake state to the sleepy state and causes decrease in alerts and conscious levels of driver. It is difficult to measure the drowsiness level directly but there are many indirect methods to detect the driver fatigue. Driver drowsiness detection can be measured using physiological measures, vehicle-based measures, behavioural measures. Physiological measures include the sure of brain wave, heart rate, pulse rate, and using the physiological signals like ECG (Electrocardiogram), EOG (Electrooculogram), EEG (Electroencephalogram) etc. Though this method measures the drowsiness accurately but it requires a physical connection with the driver such as placing several electrodes on head, chest and face which is not a convenient method and also discomfort for the driver in driving condition. Vehicle measures includes deviations from lane position, pressure on acceleration pedals, movement of the steering wheels, etc. These are constantly monitored and any change in these which crosses a threshold indicates a probability that the driver is drowsy. Behavioural measures monitors the behaviour of the driver, which includes the yawning, eye closure, eye blinking, head pose, etc. These are monitored through a camera and these drowsiness symptoms are detected. Behavioural state detection system helps to detect the drowsy driving condition early and avoid accidents. In this paper real time drowsy detection is used which is one of the best possible method to detect driver fatigue early. Real time driver detection system using image processing captures driver eyes state non- intrusively using a camera and raspberry pi is used for this. 2. LITREATURE REVIEW Drowsiness detection can be mainly classified into three aspects such as:- 1. Vehicle based measures. 2. Physiological measures. 3. Behavioral measures.