Fatin Izzati Y. Nur, M. M. Ibrahim, N. A. Manap, S. A. Nur
{"title":"Analysis of eye closure duration based on the height of iris","authors":"Fatin Izzati Y. Nur, M. M. Ibrahim, N. A. Manap, S. A. Nur","doi":"10.1109/ICCSCE.2016.7893610","DOIUrl":null,"url":null,"abstract":"Eye closure duration is one of the parameters that is regularly chosen in detecting the state of drowsiness of a person. In order to analyze the eye closure duration, a process involving eye state classification is done to classify whether the eye is in an open state or in a closed state. This paper introduces a new parameter which is the height of iris to classify the eye state and to analyze the eye closure duration. Face and eye detection is the first step and Viola-Jones algorithm is implemented in the procedure. Next, face and eye tracking is done by utilizing the Kanade Lucas Tomasi (KLT) algorithm which tracked the feature points. The extracted eye region is further used to localize the iris through image enhancement process. The most crucial step in eye closure duration is the eye state classification process. The eye state is classified based on the height of the iris that has been localized. The height of the iris is acquired from the bounding box drawn surrounding the area of the localized iris. Finally, after the state of an eye is classified successfully, the eye closure duration is analyzed through the plotted graph between the heights of the iris against a period of time. Furthermore, by evaluating the iris' height, the size of an eye for each subject is classified. The proposed algorithm is implemented on Zhejiang University (ZJU) database.","PeriodicalId":6540,"journal":{"name":"2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"85 1","pages":"419-424"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSCE.2016.7893610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Eye closure duration is one of the parameters that is regularly chosen in detecting the state of drowsiness of a person. In order to analyze the eye closure duration, a process involving eye state classification is done to classify whether the eye is in an open state or in a closed state. This paper introduces a new parameter which is the height of iris to classify the eye state and to analyze the eye closure duration. Face and eye detection is the first step and Viola-Jones algorithm is implemented in the procedure. Next, face and eye tracking is done by utilizing the Kanade Lucas Tomasi (KLT) algorithm which tracked the feature points. The extracted eye region is further used to localize the iris through image enhancement process. The most crucial step in eye closure duration is the eye state classification process. The eye state is classified based on the height of the iris that has been localized. The height of the iris is acquired from the bounding box drawn surrounding the area of the localized iris. Finally, after the state of an eye is classified successfully, the eye closure duration is analyzed through the plotted graph between the heights of the iris against a period of time. Furthermore, by evaluating the iris' height, the size of an eye for each subject is classified. The proposed algorithm is implemented on Zhejiang University (ZJU) database.
闭眼时间是检测一个人的困倦状态时经常选择的参数之一。为了分析闭眼持续时间,需要进行眼状态分类,对眼睛处于开闭状态进行分类。本文引入虹膜高度这一新的参数对人眼状态进行分类,并对闭眼时间进行分析。人脸和眼睛检测是第一步,过程中实现了Viola-Jones算法。接下来,利用Kanade Lucas Tomasi (KLT)算法对特征点进行人脸和眼动跟踪。提取的眼部区域通过图像增强处理进一步定位虹膜。闭眼时间最关键的一步是眼状态分类过程。眼睛的状态是根据虹膜的高度进行分类的。虹膜的高度由虹膜定位区域周围绘制的边界框获得。最后,在眼睛状态分类成功后,通过绘制虹膜高度与一段时间的关系图来分析闭眼持续时间。此外,通过评估虹膜的高度,对每个受试者的眼睛大小进行分类。该算法在浙江大学数据库上实现。