A computer vision framework for eye gaze tracking

Mimica, Marcio R. M.; Morimoto, Carlos H.

doi:10.1109/sibgra.2003.1241037

Cited by 16 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The points (e row , e col ) and (i row , i col ) are the row and column coordinates of the eye-region point and the iris center respectively. This vector is similar to vectors defined in the work of others who use the reference point as their method of tracking gaze [7,12].…”

Section: Calculating the Gazementioning

confidence: 85%

“…To calculate the gaze coordinate, a number of different approaches have been developed. These include artificial neural networks [3,4], point-reference techniques [2,7,10], and 3 dimensional approaches [8]. For these techniques, an accurate extraction of information -or featuresused for the calculations is paramount.…”

Section: Previous Workmentioning

confidence: 99%

“…The most popular systems to date rely on IR LEDs in order to extract the pertinent features needed for gaze calculation [7,9,10].…”

Section: Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

Gaze tracking: A sclera recognition approach

Parker

Duong

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

View full text Add to dashboard Cite

show abstract

Section: Calculating the Gazementioning

confidence: 85%

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Gaze tracking: A sclera recognition approach

Parker

Duong

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

View full text Add to dashboard Cite

show abstract

“…As shown in Equation (6), the 2nd-order polynomial function includes the 2nd-order parameters, in addition to the parameters of the 1st-order polynomial function [20,21]:

\begin{matrix} S_{x} = a \cdot {C_{x}}^{2} + b \cdot {C_{y}}^{2} + c \cdot C_{x} + d \cdot C_{y} + e \cdot C_{x} \cdot C_{y} + f \\ S_{y} = g \cdot {C_{x}}^{2} + h \cdot {C_{y}}^{2} + i \cdot C_{x} + j \cdot C_{y} + k \cdot C_{x} \cdot C_{y} + l \end{matrix}

…”

Section: Proposed Gaze-tracking Methodsmentioning

confidence: 99%

A Novel Gaze Tracking Method Based on the Generation of Virtual Calibration Points

Lee

Heo

Park

2013

Sensors

View full text Add to dashboard Cite

Most conventional gaze-tracking systems require that users look at many points during the initial calibration stage, which is inconvenient for them. To avoid this requirement, we propose a new gaze-tracking method with four important characteristics. First, our gaze-tracking system uses a large screen located at a distance from the user, who wears a lightweight device. Second, our system requires that users look at only four calibration points during the initial calibration stage, during which four pupil centers are noted. Third, five additional points (virtual pupil centers) are generated with a multilayer perceptron using the four actual points (detected pupil centers) as inputs. Fourth, when a user gazes at a large screen, the shape defined by the positions of the four pupil centers is a distorted quadrangle because of the nonlinear movement of the human eyeball. The gaze-detection accuracy is reduced if we map the pupil movement area onto the screen area using a single transform function. We overcame this problem by calculating the gaze position based on multi-geometric transforms using the five virtual points and the four actual points. Experiment results show that the accuracy of the proposed method is better than that of other methods.

show abstract

“…Current gaze tracking techniques may be classified as intrusive and non-intrusive [2]. Intrusive techniques require some kind of physical contact with the user, and non-intrusive, or remote techniques, can be done without any physical contact.…”

Section: Introductionmentioning

confidence: 99%

IR Image Based Eye Gaze Estimation

Xia

Ruan

2007

Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Com

View full text Add to dashboard Cite

In this paper, we present a novel approach to estimate the human's eye gaze from a single infrared spectrum image of two eyes. Observing that the pupil contour is a circle, we estimate the normal direction of this circle, considered as the eye gaze, from its elliptical image. By assuming that the two gaze vectors of both eyes and the vector which is determined by the two centers of the right and left pupil are coplanar, we can calculate the both eye gaze. Compared to most existing 3-D gaze estimation algorithms, our approach does not require those of face feature information such as the eye corners. Since the focal length can be calculated by our "Coplanar Constraint", ours does not need to know the exact focal length of the camera. The extensive experiments over simulated images and real images demonstrate the feasibility and the effectiveness of our method.

show abstract

A computer vision framework for eye gaze tracking

Cited by 16 publications

References 4 publications

Gaze tracking: A sclera recognition approach

Gaze tracking: A sclera recognition approach

A Novel Gaze Tracking Method Based on the Generation of Virtual Calibration Points

IR Image Based Eye Gaze Estimation

Contact Info

Product

Resources

About