TY - JOUR
T1 - High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods
AU - Batista Narcizo, Fabricio
AU - dos Santos, Fernando Eustáquio Dantas
AU - Hansen, Dan Witzner
PY - 2021/9/15
Y1 - 2021/9/15
N2 - This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the camera is far from the eye's optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye's optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5º and +0.5º. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.
AB - This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the camera is far from the eye's optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye's optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5º and +0.5º. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.
KW - high-accuracy gaze estimation
KW - uncalibrated setup
KW - gaze-mapping calibration
KW - eye-tracking
KW - eye tracker
KW - high-accuracy gaze estimation
KW - uncalibrated setup
KW - gaze-mapping calibration
KW - eye-tracking
KW - eye tracker
UR - https://github.com/fabricionarcizo/eyeinfo
UR - https://github.com/fabricionarcizo/et_simul/tree/mdpi-vision-2021
UR - https://github.com/fabricionarcizo/eye-tracking-data
U2 - 10.3390/vision5030041
DO - 10.3390/vision5030041
M3 - Journal article
SN - 2411-5150
VL - 5
SP - 1
JO - Vision
JF - Vision
IS - 3
ER -