{"title":"Eye detection and tracking in video streams","authors":"A. Fathi, M. T. Manzuri","doi":"10.1109/ISCIT.2004.1413921","DOIUrl":null,"url":null,"abstract":"This work presents a new method for eye detection in a sequence of video pictures. We first find the face region using the features of skin color, then the location of eye is determined by applying three cues on the detected face region. The first cue is the eye intensity and color, because the intensity of each eye region is relatively low and its color differs from the color of surrounding skin. The second cue is based on the position and size of eyes. The third cue is based on the convolution of the proposed eye-variance-filter with the eye-window candidates. After finding the precise location of the eye, it is tracked in a finite search window. We have evaluated and tested the presented system using different frame streams, and the results are encouraging.","PeriodicalId":237047,"journal":{"name":"IEEE International Symposium on Communications and Information Technology, 2004. ISCIT 2004.","volume":"231 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Symposium on Communications and Information Technology, 2004. ISCIT 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCIT.2004.1413921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
This work presents a new method for eye detection in a sequence of video pictures. We first find the face region using the features of skin color, then the location of eye is determined by applying three cues on the detected face region. The first cue is the eye intensity and color, because the intensity of each eye region is relatively low and its color differs from the color of surrounding skin. The second cue is based on the position and size of eyes. The third cue is based on the convolution of the proposed eye-variance-filter with the eye-window candidates. After finding the precise location of the eye, it is tracked in a finite search window. We have evaluated and tested the presented system using different frame streams, and the results are encouraging.