{"title":"Binocular model-based gaze estimation with a camera and a single infrared light source","authors":"Laura Sesma, D. W. Hansen","doi":"10.1145/3204493.3204557","DOIUrl":null,"url":null,"abstract":"We propose a binocular model-based method that only uses a single camera and an infrared light source. Most gaze estimation approaches are based on single eye models and with binocular models they are addressed by averaging the results from each eye. In this work, we propose a geometric model of both eyes for gaze estimation. The proposed model is implemented and evaluated in a simulated environment and is compared to a binocular model-based method and polynomial regression-based method with one camera and two infrared lights that average the results from both eyes. The method performs on par with methods using multiple light sources while maintaining robustness to head movements. The study shows that when using both eyes in gaze estimation models it is possible to reduce the hardware requirements while maintaining robustness.","PeriodicalId":237808,"journal":{"name":"Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3204493.3204557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We propose a binocular model-based method that only uses a single camera and an infrared light source. Most gaze estimation approaches are based on single eye models and with binocular models they are addressed by averaging the results from each eye. In this work, we propose a geometric model of both eyes for gaze estimation. The proposed model is implemented and evaluated in a simulated environment and is compared to a binocular model-based method and polynomial regression-based method with one camera and two infrared lights that average the results from both eyes. The method performs on par with methods using multiple light sources while maintaining robustness to head movements. The study shows that when using both eyes in gaze estimation models it is possible to reduce the hardware requirements while maintaining robustness.