The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 19, 2024

Filed:

Oct. 31, 2022
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Nuri Murat Arar, Zurich, CH;

Niranjan Avadhanam, Saratoga, CA (US);

Nishant Puri, San Francisco, CA (US);

Shagan Sah, Santa Clara, CA (US);

Rajath Shetty, Santa Clara, CA (US);

Sujay Yadawadkar, Santa Clara, CA (US);

Pavlo Molchanov, Mountain View, CA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/94 (2022.01); G06V 20/59 (2022.01); G06V 20/64 (2022.01); G06V 40/16 (2022.01); G06V 40/18 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/214 (2023.01); G06F 18/2193 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/95 (2022.01); G06V 20/597 (2022.01); G06V 20/647 (2022.01); G06V 40/171 (2022.01); G06V 40/193 (2022.01);
Abstract

Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.


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