High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods
Abstract
:1. Introduction
- A novel method to compensate for the influence of eye-camera location in gaze estimation based on virtual perspective camera alignment (Section 2.1). Contrary to traditional interpolation-based methods, the proposed method uses a normalized plane between the eye plane and the viewed plane to align the eye-camera in the center of the optical axis, and thus gains unrestricted eye-camera placement for uncalibrated and fully calibrated eye trackers.
- A novel method to undistort eye feature distribution on the eye plane (Section 2.2). After aligning the eye-camera onto the optical axis, the eye feature distribution will be symmetric and uniform centered in the eye feature distribution. However, due to the nonlinear projection of eyeball on the eye plane, the eye feature distribution presents a radial distortion. This method uses the distortion coefficients to reshape the eye feature distribution in an almost linear dispersion.
- This work introduces a new open-source dataset for eye-tracking studies called EyeInfo dataset (available on https://github.com/fabricionarcizo/eyeinfo, accessed on 17 August 2020). This dataset contains high-speed monocular eye-tracking data from an off-the-shelf remote eye tracker using active illumination. The data from each user has a text file with annotations concerning the eye feature, environment, viewed targets, and facial features. This dataset follows the basic principles of the General Data Protection Regulation (GDPR).
2. Materials and Methods
2.1. Eye-Camera Location Compensation Method
2.2. Eye Feature Distribution Undistortion Method
2.3. Simulated Study
2.4. User Study
2.4.1. Design
2.4.2. Eye-Tracking Data
2.4.3. Apparatus
2.4.4. Participants
2.4.5. Tasks
2.4.6. Experiment Protocol
2.4.7. Independent and Dependent Variables
2.4.8. Measures
2.4.9. Hypotheses
3. Results
3.1. Evaluation of Eye-Camera Location
3.2. Evaluation of Proposed Methods Using Simulated Data
3.3. Evaluation of Proposed Methods Using Real Data
4. Discussion
- Assuming the eye plane and the viewed plane as a stereo vision system, it is possible to use the epipolar geometry to estimate the eye-camera location in an uncalibrated setup.
- The second-order polynomial was the one that best compensates for the eye-camera location. We have tested high-order polynomials as well; however, they overfit the model and take the epipole (that represents the virtual eye-camera location) to the infinity, i.e., the epipolar lines become parallel.
- When the eye-camera is on the eye’s optical axis and moves in depth (z-axis), the shape of the eye feature distribution keeps the same while changing its scales on both x- and y-axes. It means the eye-camera location compensation method must realign the camera only on x- and y-coordinates in the three-dimensional space.
- Due to the eye-camera location, the homography-based methods have gaze-error magnitudes more significant than the interpolation-based methods.
- The proposed methods most benefit uncalibrated setups because it is not required to understand the geometry and the locations of the eye tracker components to reduce the negative influence of large and angles of the eye-camera’s optical axis into the gaze estimation.
- Both proposed methods improve the accuracy of interpolation-based eye-tracking methods using the same eye-tracking data from the gaze-mapping calibration. However, the proposed eye feature distribution undistortion method would benefit from gaining further user data, such as using more calibration data or combining with a recalibration procedure.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
PoR | Point-of-Regard |
RET | Remote Eye Trackers |
HMET | Head-Mounted Eye Trackers |
GDPR | Data Protection Regulation |
PCCR | Pupil Center-Corneal Reflection |
KDE | Kernel Density Estimation |
WCS | World Coordinate System |
Gaussian Probability Density Function | |
HMD | Head-Mounted Displays |
CNN | Convolutional Neural Networks |
DLM | Deep Learning Models |
LoS | Line of Sight |
DoF | Degrees of Freedom |
OLS | Ordinary Least Squares |
Appendix A. Gaze Estimation Methods
Appendix A.1. Appearance-Based Gaze Estimation Methods
Appendix A.2. Feature-Based Gaze Estimation Methods
Method | Description | Accuracy | Calibration | Advantages | Disadvantages |
---|---|---|---|---|---|
Homography | A planar projective mapping between the eye plane and viewed plane | – | 4 targets | It requires only four pieces of calibration data | It is more sensitive to noise, such as camera location |
Second-Order Polynomial | A regression which minimizes the sum of squared residuals | – | 9 targets | It is simple to implement and presents good accuracy | It is less accurate than homography-based methods |
Camera Compensation | A method to reshape the eye feature distribution in a normalized space | – | 9 targets | It increases the number of high-accuracy gaze estimations | The use of high-order polynomials overfits the model |
Distortion Compensation | A method to compensate for the non-coplanarity of | – | 9 targets | It presents the lowest error in real and simulated scenarios | It can blow up the estimations around the boundaries |
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Methods | Gaze | Gaze | Gaze | Average |
---|---|---|---|---|
0.58 | 0.63 | 1.00 | 0.74 | |
0.64 | 0.84 | 1.00 | 0.83 | |
0.98 | 1.00 | 1.00 | 0.99 | |
0.64 | 0.83 | 1.00 | 0.82 | |
0.63 | 0.84 | 1.00 | 0.82 | |
0.91 | 0.98 | 1.00 | 0.96 |
Methods | Gaze | Gaze | Average |
---|---|---|---|
0.50 | 0.32 | 0.41 | |
0.50 | 0.50 | 0.50 | |
0.51 | 0.62 | 0.57 | |
0.47 | 0.50 | 0.49 | |
0.49 | 0.50 | 0.50 | |
0.55 | 0.63 | 0.60 |
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Narcizo, F.B.; dos Santos, F.E.D.; Hansen, D.W. High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods. Vision 2021, 5, 41. https://doi.org/10.3390/vision5030041
Narcizo FB, dos Santos FED, Hansen DW. High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods. Vision. 2021; 5(3):41. https://doi.org/10.3390/vision5030041
Chicago/Turabian StyleNarcizo, Fabricio Batista, Fernando Eustáquio Dantas dos Santos, and Dan Witzner Hansen. 2021. "High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods" Vision 5, no. 3: 41. https://doi.org/10.3390/vision5030041