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Rendering synthetic ground truth images for eye tracker evaluation

Published: 26 March 2014 Publication History

Abstract

When evaluating eye tracking algorithms, a recurring issue is what metric to use and what data to compare against. User studies are informative when considering the entire eye tracking system, however they are often unsatisfactory for evaluating the gaze estimation algorithm in isolation. This is particularly an issue when evaluating a system's component parts, such as pupil detection, pupil-to-gaze mapping or head pose estimation.
Instead of user studies, eye tracking algorithms can be evaluated using simulated input video. We describe a computer graphics approach to creating realistic synthetic eye images, using a 3D model of the eye and head and a physically correct rendering technique. By using rendering, we have full control over the parameters of the scene such as the gaze vector or camera position, which allows the calculation of ground truth data, while creating a realistic input for a video-based gaze estimator.

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  • (2024)The Influence that the Complexity of the Three-Dimensional Eye Model Used to Generate Simulated Eye-tracking Data Has on the Gaze Estimation Errors Achieved Using the DataACM Transactions on Applied Perception10.1145/366063722:1(1-16)Online publication date: 12-Nov-2024
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cover image ACM Conferences
ETRA '14: Proceedings of the Symposium on Eye Tracking Research and Applications
March 2014
394 pages
ISBN:9781450327510
DOI:10.1145/2578153
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 26 March 2014

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Author Tags

  1. eye tracking
  2. ground truth
  3. pupil detection
  4. rendering

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ETRA '14
ETRA '14: Eye Tracking Research and Applications
March 26 - 28, 2014
Florida, Safety Harbor

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Overall Acceptance Rate 69 of 137 submissions, 50%

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  • (2025)Explain Vision Focus: Blending Human Saliency Into Synthetic Face ImagesIEEE Transactions on Multimedia10.1109/TMM.2024.352167027(489-502)Online publication date: 1-Jan-2025
  • (2024)Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation MethodsApplied Sciences10.3390/app1420958614:20(9586)Online publication date: 21-Oct-2024
  • (2024)The Influence that the Complexity of the Three-Dimensional Eye Model Used to Generate Simulated Eye-tracking Data Has on the Gaze Estimation Errors Achieved Using the DataACM Transactions on Applied Perception10.1145/366063722:1(1-16)Online publication date: 12-Nov-2024
  • (2024)Zero Shot Learning in Pupil DetectionProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3655641(1-3)Online publication date: 4-Jun-2024
  • (2024)Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00583(5921-5930)Online publication date: 3-Jan-2024
  • (2024)Appearance-Based Gaze Estimation With Deep Learning: A Review and BenchmarkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.339357146:12(7509-7528)Online publication date: Dec-2024
  • (2024)Generation of Synthetic Data for Deep Learning in Manufacturing Quality Control Systems2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)10.1109/MELECON56669.2024.10608616(74-79)Online publication date: 25-Jun-2024
  • (2024)TPDNet: A Tiny Pupil Detection Neural Network for Embedded Machine Learning Processor Arm Ethos-U55Intelligent Systems and Applications10.1007/978-3-031-47715-7_1(1-17)Online publication date: 30-Jan-2024
  • (2023)Mesh-Tension Driven Expression-Based Wrinkles for Synthetic Faces2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00351(3504-3514)Online publication date: Jan-2023
  • (2023)Ground Truth Data Generator in Automotive Infrared Sensor Vision Problems Using a Minimum Set of OperationsAdvances in Computational Collective Intelligence10.1007/978-3-031-41774-0_50(632-644)Online publication date: 22-Sep-2023
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