{"id":167517,"date":"2014-09-01T00:00:00","date_gmt":"2014-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/eye-gaze-tracking-using-an-rgbd-camera-a-comparison-with-an-rgb-solution\/"},"modified":"2018-10-16T21:54:05","modified_gmt":"2018-10-17T04:54:05","slug":"eye-gaze-tracking-using-an-rgbd-camera-a-comparison-with-an-rgb-solution","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/eye-gaze-tracking-using-an-rgbd-camera-a-comparison-with-an-rgb-solution\/","title":{"rendered":"Eye Gaze Tracking Using an RGBD Camera: A Comparison with an RGB Solution"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Most commercial eye gaze tracking systems are based on the use of infrared lights. However, such systems may not work outdoor or may have a very limited head box for them to work. This paper proposes a non-infrared based approach to track one&#8217;s eye gaze with an RGBD camera (in our case, Kinect). The proposed method adopts a personalized 3D face model constructed off-line. To detect the eye gaze, our system tracks the iris center and a set of 2D facial landmarks whose 3D locations are provided by the RGBD camera. A simple onetime calibration procedure is used to obtain the parameters of the personalized eye gaze model. We compare the performance of the proposed method against the 2D approach using only RGB input on the same images, and find that the use of depth information directly from Kinect achieves more accurate tracking. As expected, the results from the proposed method are not as accurate as the ones from infrared-based approaches. However, this method has the potential for practical use with upcoming better and cheaper depth cameras.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most commercial eye gaze tracking systems are based on the use of infrared lights. However, such systems may not work outdoor or may have a very limited head box for them to work. This paper proposes a non-infrared based approach to track one&#8217;s eye gaze with an RGBD camera (in our case, Kinect). The proposed [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM - Association for Computing Machinery","msr_publisher_other":"","msr_booktitle":"The 4th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2014)","msr_chapter":"","msr_edition":"The 4th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2014)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version can be found at http:\/\/dl.acm.org.","msr_conference_name":"The 4th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2014)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"X. Xiong, Q. Cai, Z. Liu, Z. 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