Eye detection and coarse localization of pupil for video-based eye tracking systems
A video-based eye tracking system generally captures NIR images, each of which contains one or two eyes of a subject. The subject’s point of gaze is then determined using 3D eye model and pupil centre corneal reflection technique. Eye detection and pupil localization play significant roles in video-...
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| Published in | Expert systems with applications Vol. 236; p. 121316 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.02.2024
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| Abstract | A video-based eye tracking system generally captures NIR images, each of which contains one or two eyes of a subject. The subject’s point of gaze is then determined using 3D eye model and pupil centre corneal reflection technique. Eye detection and pupil localization play significant roles in video-based eye tracking systems. However, face rotation, wearing glasses, eye-shape variation and illumination variation make it difficult to detect an eye and localize a pupil accurately in the images captured by video-based eye tracking systems. In this paper, we proposed an eye detector that adopts a coarse-to-fine strategy. The eye detector consists of three classifiers: an ATLBP-THACs feature-based cascade classifier, a branch CNN and a multi-task CNN. We also proposed a method for coarse pupil localization. Coarse localization is an important step for pupil localization since it can provide initial pupil coordinates for a fine pupil localization method. Given a downscaled eye image, a shallow CNN is used to estimate the location of seven landmarks. On this basis, pupil center and radius are estimated. A method for small dim target enhancement is used to increase the contrast between pupil and background. The main goal of pupil enhancement is to make it easier to binarize an eye image. At last, component filtering is made by utilizing the estimated pupil center and radius. We collected a dataset named neepuEYE dataset that consists of 5500 NIR eye images from 109 people. The images can be used to generate augmented samples for training an eye detector since they contain eyes with different shape, orientation and pupil localization. Experimental results show that our eye detector is a fast and robust detector. Furthermore, our method for coarse pupil localization can obtain not only high detection rate but also high localization speed. |
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| AbstractList | A video-based eye tracking system generally captures NIR images, each of which contains one or two eyes of a subject. The subject’s point of gaze is then determined using 3D eye model and pupil centre corneal reflection technique. Eye detection and pupil localization play significant roles in video-based eye tracking systems. However, face rotation, wearing glasses, eye-shape variation and illumination variation make it difficult to detect an eye and localize a pupil accurately in the images captured by video-based eye tracking systems. In this paper, we proposed an eye detector that adopts a coarse-to-fine strategy. The eye detector consists of three classifiers: an ATLBP-THACs feature-based cascade classifier, a branch CNN and a multi-task CNN. We also proposed a method for coarse pupil localization. Coarse localization is an important step for pupil localization since it can provide initial pupil coordinates for a fine pupil localization method. Given a downscaled eye image, a shallow CNN is used to estimate the location of seven landmarks. On this basis, pupil center and radius are estimated. A method for small dim target enhancement is used to increase the contrast between pupil and background. The main goal of pupil enhancement is to make it easier to binarize an eye image. At last, component filtering is made by utilizing the estimated pupil center and radius. We collected a dataset named neepuEYE dataset that consists of 5500 NIR eye images from 109 people. The images can be used to generate augmented samples for training an eye detector since they contain eyes with different shape, orientation and pupil localization. Experimental results show that our eye detector is a fast and robust detector. Furthermore, our method for coarse pupil localization can obtain not only high detection rate but also high localization speed. |
| ArticleNumber | 121316 |
| Author | Chen, Jie-chun Zhao, Li-ping Yao, Chun-ying Qiao, Yu-yang Yu, Pin-qing |
| Author_xml | – sequence: 1 givenname: Jie-chun orcidid: 0000-0002-2361-3104 surname: Chen fullname: Chen, Jie-chun email: chenjiechun@neepu.edu.cn organization: College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China – sequence: 2 givenname: Pin-qing surname: Yu fullname: Yu, Pin-qing organization: College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China – sequence: 3 givenname: Chun-ying surname: Yao fullname: Yao, Chun-ying organization: College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China – sequence: 4 givenname: Li-ping surname: Zhao fullname: Zhao, Li-ping organization: College of Science, Northeast Electric Power University, Jilin 132012, China – sequence: 5 givenname: Yu-yang surname: Qiao fullname: Qiao, Yu-yang organization: College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China |
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| Cites_doi | 10.1016/j.eswa.2016.09.036 10.1016/j.jneumeth.2019.05.016 10.1007/s00138-016-0776-4 10.1109/ACCESS.2017.2735633 10.1109/TIP.2013.2286328 10.1145/2168556.2168585 10.1007/s11760-020-01710-7 10.1016/S0262-8856(99)00053-0 10.5244/C.8.42 10.1007/s11042-016-4334-x 10.1109/ACCESS.2021.3052851 10.1109/TPAMI.2009.30 10.1109/TIP.2010.2042645 10.1155/2017/8718956 10.1109/TBME.2005.863952 10.3233/IDA-173361 10.1109/ISMAR52148.2021.00053 10.1007/978-3-319-23192-1_4 10.1016/j.neunet.2021.03.019 10.1109/CMVIT57620.2023.00018 10.1145/2857491.2857505 10.1016/j.eswa.2021.116004 10.1145/2857491.2857520 10.1016/j.jvcir.2018.07.013 10.1016/S0169-2607(98)00105-9 10.1080/09500340701467827 10.1016/j.patcog.2009.12.023 10.1016/j.cviu.2018.02.002 |
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| Keywords | Eye tracking NIR Eye detection Gaze tracking Pupil localization |
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| Title | Eye detection and coarse localization of pupil for video-based eye tracking systems |
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