3D gaze estimation without explicit personal calibration

Wang, Kang; Ji, Qiang

doi:10.1016/j.patcog.2018.01.031

Cited by 40 publications

(28 citation statements)

References 19 publications

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“…The research on the regression-based method mainly concentrates on improving the precision and simplifying the steps of personal calibration. Regression-based methods can be divided into two categories [18]: feature-based methods and appearance-based methods. Feature-based methods establish mappings between eye features and eye directions, such as pupils, reflective spots, and so on [19,20].…”

Section: Related Workmentioning

confidence: 99%

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

et al. 2018

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This paper proposes a 3D point-of-regard estimation method based on 3D eye model and a corresponding head-mounted gaze tracking device. Firstly, a head-mounted gaze tracking system is given. The gaze tracking device uses two pairs of stereo cameras to capture the left and right eye images, respectively, and then sets a pair of scene cameras to capture the scene images. Secondly, a 3D eye model and the calibration process are established. Common eye features are used to estimate the eye model parameters. Thirdly, a 3D point-of-regard estimation algorithm is proposed. Three main parts of this method are summarized as follows: (1) the spatial coordinates of the eye features are directly calculated by using stereo cameras; (2) the pupil center normal is used to the initial value for the estimation of optical axis; (3) a pair of scene cameras are used to solve the actual position of the objects being watched in the calibration process, and the calibration for the proposed eye model does not need the assistance of the light source. Experimental results show that the proposed method can output the coordinates of 3D point-of-regard more accurately.

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Section: Related Workmentioning

confidence: 99%

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

et al. 2018

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“…Categories System configuration Head movement Accuracy/ • Special features [1] Appearance-based 1 0 2.37±1.42 Using eye appearance [24] ICT-based 1 0 × X:0.99,Y:1.33 Polynomial function between iris-corner and PoR [5] ICCR-based 1 1 × 0.75 Polynomial function between iris-glint and PoR [25] CR-based 1 5 0.70 Using a dynamic tangent plane [6] HN-based 1 4 × 1.03-1.17 One point calibration [26] 3D model-based 1 0 X:3.0,Y:4.5 Using an RGB-D camera(Kinect) [27] 3D model-based 1 4 1.3 Without explicit personal calibration [28] 3D model-based 2 2 1.18 Using glint and contour features [11] 3D model-based 2 3 1 A linear solution for LoS estimation Our 3D model-based 1 2 X:1.17,Y:1.17 Typical OCTLS system on the OA of the eyeball. When the cornea radius deviates from the true value by 0.5 mm, the OA error is still less than 0.1 • .…”

Section: Methodsmentioning

confidence: 99%

Cornea Radius Calibration for Remote 3D Gaze Tracking Systems

et al. 2020

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Cornea radius estimation is a key technique for 3D gaze estimation in the single-camera 3D gaze tracking system. Traditional methods with one-camera-one-light-source systems or one-camera-twolight-source systems cannot achieve 3D gaze estimation. The 3D line-of-sight can be estimated only when the cornea radius is pre-calibrated by the user. A cornea radius calibration method based on the iris radius is proposed in this paper for 3D gaze estimation in remote one-camera-two-light-source systems. We first calibrate the iris radius based on the binocular strategy, estimate the spatial iris center using the calibrated iris radius, and then calibrate the cornea radius by a set of non-linear equations under the constraint of equivalent distances from the cornea center to the iris edge points. The calibrated cornea radius is verified by binocular optimization constraints. Simulations and physical experiments validate the effectiveness of the proposed method. The iris-based cornea radius calibration approach is novel; it can be used to obtain the cornea radius and 3D gaze using remote one-camera-one-light-source or one-camera-multi-light-source systems.

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“…Some methods use not only a pure saliency maps but utilize also natural constraints like center bias that assumes that gaze locations are biased towards the center of the scene during free viewing [19,20].…”

Section: The State Of the Artmentioning

confidence: 99%

Implicit Calibration Using Probable Fixation Targets

Kasprowski

Harężlak

Skurowski

2018

Preprint

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Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user input device. Classic calibration methods taking time and imposing unnatural behavior of users have to be replaced by intelligent methods that are able to calibrate the signal without conscious cooperation with users. Such an implicit calibration requires some knowledge about the stimulus a person is looking at and takes into account this information to predict probable gaze targets. The paper describes one of the possible methods to perform implicit calibration: it starts with finding probable fixation targets (PFTs), then uses these targets to build a mapping - probable gaze path. Various possible algorithms that may be used for finding PFTs and mapping are presented in the paper and errors are calculated utilizing two datasets registered with two different types of eye trackers. The results show that although for now the implicit calibration provides results worse than the classic one, it may be comparable with it and sufficient for some applications.

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3D gaze estimation without explicit personal calibration

Cited by 40 publications

References 19 publications

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

Cornea Radius Calibration for Remote 3D Gaze Tracking Systems

Implicit Calibration Using Probable Fixation Targets

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