Automatic Calibration Of A Gaze Direction Algorithm From User Behavior

• US 20110310006A1
• Filed: 06/22/2011
• Published: 12/22/2011
• Est. Priority Date: 12/22/2008
• Status: Active Grant

• Current Assignee: Seeing Machines LTD
• Original Assignee: Seeing Machines LTD
• Inventors: Edwards, Timothy James Henry, Langdale-Smith, Nicholas John
• Granted Patent: US 8,982,046 B2
• Time in Patent Office: Days
• US Class Current: 345/156
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06V 40/19 Sensors therefor

Abstract

A method of calibrating the eye gaze direction of a user, the method including the steps of: (a) monitoring a user'"'"'s eye gaze direction whilst carrying out a series of predetermined tasks, each of the tasks having an expected subject gaze direction; and (b) correlating the user'"'"'s eye gaze direction with the expected direction for a statistically significant period of time; (c) calculating, from the correlating step, a series of likely eye gaze direction usage parameters associated with the user.

Claims

1. A method of calibrating the eye gaze direction of a user, the method comprising the steps of:

(a) monitoring a user'"'"'s eye gaze direction whilst carrying out a series of known tasks, each of said tasks having an expected subject gaze direction;

(b) correlating the user'"'"'s eye gaze direction with the expected direction for a statistically significant period of time; and

(c) calculating, from said correlating step, a series of likely eye gaze direction usage parameters associated with said user.

2. A method as claimed in claim 1 wherein said tasks include at least one of interacting with a computer graphical user interface or driving a vehicle.

3. A method as claimed in claim 1 further comprising the steps of:

for predetermined users, storing first eye gaze direction usage parameters associated with a user'"'"'s first utilisation of the method;

for a new user of the method, determining if the user has a previous history of usage of the method; and

for users utilising the method having a previous history of usage of the method, utilising the corresponding first eye gaze direction usage parameters as an initial estimate of the calibration.

4. A method as claimed in claim 1 wherein said correlating step includes rejection of outlying data sets.

5. A method as claimed in claim 1 wherein said predetermined tasks include clicking on an icon in a graphical user interface for a computer.

6. A method as claimed in claim 1 wherein said predetermined tasks include typing within a program for a computer.

7. A method as claimed in claim 1 wherein said correlating step further comprises the steps of:

(i) forming a histogram of measurements of user'"'"'s gaze direction;

(ii) determining likely peaks in the histogram measurements; and

(iii) correlating the peaks with known objects in the environment around the user.

8. A system for calibrating the eye gaze direction of a user, the system including:

eye gaze direction monitoring unit for monitoring a user'"'"'s eye gaze direction whilst carrying out a series of known tasks, each of said tasks having an expected subject gaze direction; and

processing means for correlating the user'"'"'s eye gaze direction with an expected direction for a statistically significant period of time; and

calculation means interconnected to said processing means for calculating, from said correlating step, a series of likely eye gaze direction usage parameters associated with said user.

9. A method of calibrating the eye gaze direction of a user, the method comprising the steps of:

(a) monitoring a user'"'"'s eye gaze direction whilst the user carries out a series of tasks, each of said tasks having an expected subject gaze direction at items of interest in the environment of the tasks;

(b) building a histogram of user gaze direction values during the monitoring;

(c) correlating peaks in the histogram with items of interest in the environment of the tasks;

(d) calculating, from said correlating step, a series of likely eye gaze direction usage parameters associated with said user.

10. A method as claimed in claim 9 wherein said step (b) includes building a histogram of user gaze time verses gaze direction.

11. A method as claimed in claim 9 wherein said series of tasks include driving a vehicle.

12. A method as claimed in claim 9 wherein said series of tasks include interacting with a computer graphical display.