IP Library › Granted Patent US 12,288,417
Granted Patent B2
US 12,288,417 · App. 18/824,578 · Granted Apr 29, 2025

Obtaining high-resolution oculometric parameters

Inventors: Edmund Ben-Ami (Tel Aviv, IL); Micha Yochanan Breakstone (Austin, TX); Rotem Zvi Bar-Or (Boulder, CO); Vladimir Anisimov (Tel-Aviv, IL)
Assignee: NeuraLight, Ltd.
G06V40/18A61B3/0025A61B3/113A61B5/163G06F3/013G06T7/0012G06T2207/20081G06T2207/30041
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Quick Facts
Patent No.
US 12,288,417
App. No.
18/824,578
Granted
Apr 29, 2025
Kind
B2
Abstract

Disclosed are systems and methods for extracting high resolution oculometric parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius) at a first resolution. A deconvolution process is performed on the video stream to improve accuracy or resolution of the oculometric parameters, based on stimulus information of a video stimulus displayed on a client device associated with the user, environment data of an environment in which the user is located, device data of the client device, etc. The oculometric parameters are then processed using a prediction model that is trained based on high resolution oculometric parameters obtained using eye tracking devices to predict oculometric parameters at a resolution greater than the first resolution.

Claims (50)

1. A method comprising:

obtaining a video stream from a client device associated with a user, the video stream including a video of a face of the user;

providing as input the video stream to a first prediction model to obtain a first set of oculometric parameters of an eye of the user, the first set of oculometric parameters including first iris data or first eyelid data, wherein the first set of oculometric parameters are obtained at a first resolution;

deconvolving the video stream based on the first eyelid data, stimulus data, or environment data to obtain adjusted eyelid data as part of the first set of oculometric parameters, the stimulus data including spatio-temporal information or optical information of a video stimulus presented on a display associated with the client device, the environment data including light conditions in an environment the client device is in; and

providing as input the first set of oculometric parameters to a second prediction model to obtain a second set of oculometric parameters at a second resolution, wherein the second set of oculometric parameters includes second iris data including an iris radius, an iris translation, or an iris rotation, and wherein the second resolution is greater than the first resolution.

2. The method of claim 1 , wherein the first resolution is measured in terms of a pixel or other lower resolution value and the second resolution is measured in terms of a sub-pixel value.

3. The method of claim 1 , wherein providing the input to the second prediction model includes:

training the second prediction model using a plurality of datasets to predict the second set of oculometric parameters at the second resolution, wherein each dataset includes a video of a face of a specified user, a first set of values corresponding to the first set of oculometric parameters obtained at the first resolution, and a set of labels having a second set of values corresponding to the second set of oculometric parameters obtained at the second resolution.

4. The method of claim 3 , wherein the second set of values corresponding to the second set of oculometric parameters is obtained using an eye-tracking device that is configured to obtain the second set of oculometric parameters at the second resolution for a video of the face of the specified user.

5. The method of claim 1 , wherein deconvolving the video stream includes:

determining a point spread function of a deconvolution process based on the first eyelid data, the stimulus data, or the environment data; and

deconvolving the video stream using the point spread function to obtain the adjusted eyelid data.

6. The method of claim 5 , wherein deconvolving the video stream includes determining the adjusted eyelid data based on a maximal likelihood of a prediction of a location of a first pupil center and an eyelid boundary.

7. The method of claim 5 , wherein determining the adjusted eyelid data includes:

processing the first eyelid data using a maximal likelihood estimation curve fitting operation to obtain a parabolic fit of an eyelid of the eye, and

obtaining a plurality of coordinates along the parabolic fit as the adjusted eyelid data.

8. The method of claim 1 , wherein providing the video stream to the first prediction model to obtain the first set of oculometric parameters further includes:

obtaining user data associated with the user, the user data including optometric data of the eye of the user, and

applying corrections to the first set of oculometric parameters based on the user data.

9. The method of claim 1 further comprising:

determining an additional set of oculometric parameters based on the second set of oculometric parameters, the additional set of oculometric parameters including at least one of a pupil visible fraction, an iris visible fraction, a pupil coverage asymmetry, or an iris coverage asymmetry.

10. A non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method, the method comprising:

obtaining a video stream from a client device associated with a user, the video stream including a video of a face of the user;

providing as input the video stream to a first prediction model to obtain a first set of oculometric parameters of an eye of the user, the first set of oculometric parameters including first iris data or first eyelid data;

deconvolving the video stream based on the first eyelid data, stimulus data, or environment data to obtain adjusted eyelid data as part of the first set of oculometric parameters, the stimulus data including spatio-temporal information or optical information of a video stimulus presented on a display associated with the client device, the environment data including light conditions in an environment the client device is in; and

providing as input the first set of oculometric parameters to a second prediction model to obtain a second set of oculometric parameters at a greater resolution than the first set of oculometric parameters, wherein the second set of oculometric parameters includes an iris radius, an iris translation, or an iris rotation.

11. The computer-readable medium of claim 10 , wherein providing the input to the second prediction model includes:

training the second prediction model using a plurality of datasets to predict the second set of oculometric parameters, wherein each dataset includes a video of a face of a specified user, a first set of values corresponding to the first set of oculometric parameters obtained at a specified resolution, and a set of labels having a second set of values corresponding to the second set of oculometric parameters obtained at a resolution greater than the specified resolution.

12. The computer-readable medium of claim 11 , wherein the second set of values corresponding to the second set of oculometric parameters is obtained using an eye-tracking device that is configured to obtain the second set of oculometric parameters at a greater resolution than the specified resolution.

13. The computer-readable medium of claim 10 , wherein deconvolving the video stream includes:

determining a point spread function of a deconvolution process based on the first eyelid data the stimulus data or the environment data; and

deconvolving the video stream using the point spread function to obtain the adjusted eyelid data.

14. The computer-readable medium of claim 13 , wherein deconvolving the video stream includes determining the adjusted eyelid data based on a maximal likelihood of a prediction of a location of a first pupil center and an eyelid boundary.

15. The computer-readable medium of claim 14 , wherein determining the adjusted eyelid data includes:

processing the first eyelid data using a maximal likelihood estimation curve fitting operation to obtain a parabolic fit of an eyelid of the eye; and

obtaining a plurality of coordinates along the parabolic fit as the adjusted eyelid data.

16. A system, comprising:

one or more memories; and

at least one processor each coupled to at least one of the memories and configured to perform operations comprising:

obtaining a video stream from a client device associated with a user, the video stream including a video of a face of the user;

providing as input the video stream to a first prediction model to obtain a first set of oculometric parameters of an eye of the user, the first set of oculometric parameters including first iris data or first eyelid data, wherein the first set of oculometric parameters are obtained at a first resolution;

deconvolving the video stream based on the first eyelid data, stimulus data or environment data to obtain adjusted eyelid data as part of the first set of oculometric parameters, the stimulus data including spatio-temporal information or optical information of a video stimulus presented on a display associated with the client device, the environment data including light conditions in an environment the client device is in; and

providing as input the first set of oculometric parameters to a second prediction model to obtain a second set of oculometric parameters at a second resolution, wherein the second set of oculometric parameters includes second iris data including an iris radius, an iris translation, or an iris rotation, and wherein the second resolution is greater than the first resolution.

17. The system of claim 16 , wherein the first resolution is measured in terms of a pixel or other lower resolution value and the second resolution is measured in terms of a sub-pixel value.

18. The system of claim 16 , wherein providing the input to the second prediction model includes:

training the second prediction model using a plurality of datasets to predict the second set of oculometric parameters at the second resolution, wherein each dataset includes a video of a face of a specified user, a first set of values corresponding to the first set of oculometric parameters obtained at the first resolution, and a set of labels having a second set of values corresponding to the second set of oculometric parameters obtained at the second resolution.

19. The system of claim 18 , wherein the second set of values corresponding to the second set of oculometric parameters is obtained using an eye-tracking device that is configured to obtain the second set of oculometric parameters at the second resolution for a video of the face of the specified user.

20. The system of claim 16 , wherein deconvolving the video stream includes:

determining a point spread function of a deconvolution process based on the first eyelid data, the stimulus data, or the environment data; and

deconvolving the video stream using the point spread function to obtain the adjusted eyelid data.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 9, 2024
From: BEN-AMI, EDMUND; BREAKSTONE, MICHA YOCHANAN; BAR-OR, ROTEM ZVI; ANISIMOV, VLADIMIR
To: NEURALIGHT LTD.
Reel/Frame 068527/0116 →
Continuity (3)
Continuation 17722095 · Apr 15, 2022
Provisional Application 63183388 · May 3, 2021
Related Publication 20240428618A1 · Dec 26, 2024
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