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Short Paper Proc. of Int. Conf. on Advances in Computer Engineering 2012

Automatic Fatigue Detection Based On Eye States Saeid Fazli, Parisa Esfehani Research Institute of Physiology and Biotechnology, University of Zanjan, Zanjan, Iran Email: fazli@znu.ac.ir Electrical Engineering Department, University of Zanjan , Zanjan, Iran Email: p_esfehani@yahoo.com Abstract— In this paper, we propose a method for automatics fatigue detection based on eye-tracking. In the present paper we use CCD camera for capturing image from drivers ‘face. The proposed method consists of four steps. In the first step we detect face area using YCbCr color-space, and then crop the image according to calculated maximum and minimum values of vertical and horizontal. based on the obtained results, generally eyes located in the two-fifth to three-fifth of the upper region of face, So we can crop image in this area, and estimate eyes region. In the third step determine exact location of eyes and track them whit use changing pixels of white to black. In the last step counter the number of white and black pixel. In the initial frame we posit driver is awaked, and consideration these number as threshold, so in another frame compare number of white and black pixel whit threshold and determine is driver sleepy or not..

experimental results in section 6. Conclusion is discussed in last section. II.

In the past two decades several studies have been performed in this field. There are various techniques for doing this work; some of them use physiological information, another one check vehicle behavioral and last one investigate driver behavioral. Among these methods the last one is better than the others. When driver fatigue occurs, visual behaviors can be easily observed from changes in their facial features especially from their eyes. It is indicated that the change regularity of eye states have high relativity with the driver’s mental states [5]. Zheng pei calculated the ratio of eye closing during a period of time. The ratio can reflect driver’s vigilance level [6]. Wenhui Dong proposed a method to detect the distance of eyelid, then judged the driver’s status by this kind of information [5]. Nikolaos P used front view and side view images to precisely locate eyes [7]. Edge detection and gray-level projection methods were also applied for the eyes location by Wen-Bing Horng [8]. Zutao Zhang located the face by using Haar algorithm and proposed an eye tracking method based on Unscented Kalman Filter [9]. Abdelfattah Fawky presented a combination of algorithms, namely wavelets transform, edge detection and YCrCb transform in the eye detection [10]. Qiang Ji depended on IR illumination to locate eyes [11]. In [12], the authors used the symmetric property of faces to detect facial area on an image. Then, they used pixel difference to find the edges on the facial region to locate the vertical position of the eyes. Since the edge detection they used cannot clearly mark edges, it is not easy to locate accurate ocular locations. The authors used thresholding to improve the location of the ocular places. A fier finding the approximate eyes positions, a concentric circle template was designed to locate the exact eyes locations, and the template was used to track eyes in the following images. Face symmetry is an obvious feature for an upright face. However, it usually fails to locate the correct face [13].

Index Terms— Introduction, previous worked , overal flowchart, face detection, eyes detection and localization, fatigue detection, experimental results, conclusion, references

I. INTRODUCTION In recent years issue of vehicle safety has special significance and recently many investigations have been conducted on it. Intelligent Driver Monitoring Systems is one this items that has been considered in vehicle safety. As this systems with intelligent recognize accidental conditions, try to help and warning to drivers. One of the most important equipment for smart car is Driver Fatigue detection system that has great importance in restrain road and fatal accidents. The National Highway Traffic Safety Administration (NHTSA) of USA estimates that there are annually about 100,000 crashes in USA that are caused by fatigue and result in more than 1500 fatalities and 71,000 injuries [1]. Some studies have demonstrated that the driver drowsiness accounts for 16% of all crashes and over 20% of the crashes in the highways [2]. Thus, the driver fatigue assessment remains to be a big challenge to meet the demands of future intelligent transportation systems [3]. Developing a system that actively monitors the driver’s fatigue level in real time (and produces alarm signals when necessary), is important for the prevention of accidents, and this is the main motivation of our paper [4]. The reminder of this paper is organized as follows: Section 2 provides a report survey of related researches on driver fatigue detection. In section 3 described a system for face detection. Explain proposed system for eye detection in section 4. Section 5 described a new method of Fatigue Detection by measuring the number of black pixels in eye region. Shows © 2012 ACEEE DOI: 02.ACE.2012.03. 22

PREVIOUS WORKED

III. OVERAL

FLOWCHART

The purposed algorithm is based on changes of eyes state. It considers 6 steps that show in Fig.1. At first we received a video film of drivers’ face so we should convert it to consecutive frames of image, and these images are input for our worked. 77


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