US 12,112,573 B2
Asymmetric facial expression recognition
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); and Linjie Luo, Los Angeles, CA (US)
Assigned to Lemon Inc., Grand Cayman (KY)
Filed by Lemon Inc., Grand Cayman (KY)
Filed on Aug. 13, 2021, as Appl. No. 17/402,344.
Prior Publication US 2023/0046286 A1, Feb. 16, 2023
Int. Cl. 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)
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)] 17 Claims
OG exemplary drawing
 
1. A method, comprising:
determining a first loss function 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;
determining a second loss function 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;
determining, based on the first loss function and the second loss function, a maximum loss function;
applying the maximum loss function during training of a model, wherein the trained model is configured to predict different types of asymmetric facial expressions in input images; and
controlling a facial animation using the predicted different types of asymmetric facial expressions.