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:
Oct. 08, 2024

Filed:

Aug. 13, 2021
Applicant:

Lemon Inc., Grand Cayman, KY;

Inventors:

Michael Leong Hou Tay, Los Angeles, CA (US);

Wanchun Ma, Los Angeles, CA (US);

Shuo Cheng, Los Angeles, CA (US);

Chao Wang, Los Angeles, CA (US);

Linjie Luo, Los Angeles, CA (US);

Assignee:

Lemon Inc., Grand Cayman, KY;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06F 18/21 (2023.01); G06T 7/246 (2017.01); G06T 13/40 (2011.01); G06T 13/80 (2011.01); G06V 10/24 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06V 40/176 (2022.01); G06F 18/2193 (2023.01); G06T 7/251 (2017.01); G06T 13/40 (2013.01); G06T 13/80 (2013.01); G06V 10/242 (2022.01); G06V 40/171 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01);
Abstract

The present disclosure describes techniques for facial expression recognition. A first loss function may be determined based on a first set of feature vectors associated with a first set of images depicting facial expressions and a first set of labels indicative of the facial expressions. A second loss function may be determined based on a second set of feature vectors associated with a second set of images depicting asymmetric facial expressions and a second set of labels indicative of the asymmetric facial expressions. The first loss function and the second loss function may be used to determine a maximum loss function. The maximum loss function may be applied during training of a model. The trained model may be configured to predict at least one asymmetric facial expression in a subsequently received image.


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