Implementation of face and eye detection on DM6437 board using simulink model

B Vijayalaxmi,Seifedine Kadry,P Chandana,N Neelima,Maytham N Meqdad,Kaushik Sekaran

doi:10.11591/eei.v9i2.1703

Abstract

Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

Highlights

Driver drowsiness is a major factor in more number of road accidents
This research work focuses on tracking of eyes looking at the face of the image captured using an efficient image-processing algorithm
The purpose of this research work is to develop a driver fatigue detection system using vision-based approach based on eye blink rate

Summary

Introduction

Driver drowsiness is a major factor in more number of road accidents. According to recent statistics annually nearly 1,200 deaths and 76,000 injuries are related to driver drowsiness. The proposed algorithm focuses on designing a system, which accurately monitors the status of driver’s eyes. Drowsiness of driver is first observed in eyes; the algorithm is designed to monitor the driver’s eyes to avoid on road accidents. Drowsiness detection involves sequence of steps such that detection of face, observation of eye movements and eye blink rate. This research work focuses on tracking of eyes looking at the face of the image captured using an efficient image-processing algorithm. After successful detection of eyes form the capture image, the system is designed to know the status of the eyes to detect driver drowsiness. Most of the applications such as facial recognition, accident avoidance systems, virtual tools and human identification systems involve the analysis of face images. Monitoring status of eyes and gaze detection can be used to avoid on road accidents

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