Objective

2

Gaze estimation can be divided into 2D and 3D gaze estimation. The 2D gaze estimation based on polynomial mapping uses only single-eye pupil center cornea reflection (PCCR) vector information to calculate the 2D (

x

y

) point of regard (POG) in a plane. The 3D gaze estimation based on binocular lines of sight intersection needs to use the PCCR vector information of both eyes and the 3D coordinate of the left and right eyeball optical centers (the point at which eye sight is emitted) to calculate 3D (

x

y

z

) POG in a 3D space. In the process of 3D gaze estimation

the measurement error exists as a result of manual measurement of the 3D coordinates of the eyeball optical center and the large deviation of the 3D gaze estimation results in the direction of depth. On the basis of the traditional binocular lines of the sight intersection method for 3D gaze estimation

we propose two primary improvements. We use a calibration method to obtain the 3D coordinates of the eyeball optical center to replace manual measurement. Then

we use data filtering in-depth direction and

Z

-plane intercepting correction method to correct the 3D gaze estimation results.

Method

2

First

the subject gazes at nine marked points on a calibration plane

which is at the first distance away from human eyes

and an infrared camera in front of the subject is used to capture eye images. The image processing algorithm can obtain the PCCR vector information of both eyes. The mapping functions of both eyes on the first plane can be solved according to the second-order polynomial mapping principle between the PCCR vector and the plane marked points. Second

with the calibration plane moved to a second distance

the subject gazes at the nine marked points again. With the use of the mapping functions of both eyes

the 2D POG of both eyes at the first calibrated distance can be calculated

and the nine marked points at the second distance to the left and right 2D POG at the first calibrated distance can be connected. Multiple lines will intersect at two points

and calculating these two equivalent intersection points obtains the calibration result of the 3D coordinates of the eyeball optical center. Third

3D gaze estimation can be performed. With the left and right planar 2D POG combined with the 3D coordinates of the eyeball optical center and with the establishment of an appropriate space coordinate system (taking the calibration plane as the

X

and

Y

plane and taking the depth of the distance as the

Z

axis)

the lines of sight of both eyes can be calculated. According to the principle of human binocular vision

both eyes' lines of sight will intersect at one point in space

and calculating the intersection point can obtain the rough 3D POG. The binocular vision lines are generally disjoint due to calculation and measurement errors. Thus

the midpoint of the common perpendicular should be chosen as the intersection. Finally

for the larger jitter of the resultant in-depth direction

the proposed data filtering in-depth direction and

Z

-plane intercepting correction method is used to correct the rough result. In this method

the data sequence of depth distance direction (

Z

coordinate) is first filtered. Using the filtered distance result generates a plane that is perpendicular to the

Z

axis. Then

the plane intercepts the left and right lines of sight to obtain two points

and the midpoint of two points is chosen as the correction result of the other two directions (

X

and

Y

). After this filtering and correction process

a more accurate 3D POG can be obtained.

Result

2

We use two different sizes of workspaces to test the proposed method

and the experiment result shows that in the small workspace (24×18×20 cm

3

)

the work distance in-depth direction is 3050 cm

the angular average error is 0.7°

and the Euclidean distance average error is 17.8 mm. By contrast

in the large workspace (60×36×80 cm

3

)

the work distance in-depth direction is 50130 cm

the angular average error is 1.0°

and the Euclidean distance average error is 117.4 mm. Compared with other traditional 3D gaze estimation methods

the proposed method considerably reduces the angle and distance deviation under the same distance testing condition.

Conclusion

2

The proposed calibration method for the eyeball optical center can obtain the 3D coordinates of the eyeball optical center conveniently and accurately. The method can avoid the eyeball optical center measurement error introduced by manual measurement and reduce the angle deviation of 3D POG significantly. The proposed data filtering in-depth direction and

Z

-plane intercepting correction method can reduce the jitter of the 3D POG result in-depth direction and can reduce the distance deviation of 3D POG significantly. This method is of great significance for the practical application of 3D gaze.