US 12,299,905 B2
Region-based stabilized face tracking
Chen Cao, Los Angeles, CA (US); Menglei Chai, Los Angeles, CA (US); Linjie Luo, Los Angeles, CA (US); and Oliver Woodford, Santa Monica, CA (US)
Assigned to Snap Inc., Santa Monica, CA (US)
Filed by Snap Inc., Santa Monica, CA (US)
Filed on Aug. 18, 2023, as Appl. No. 18/235,563.
Application 18/235,563 is a continuation of application No. 17/248,908, filed on Feb. 12, 2021, granted, now 11,769,259.
Application 17/248,908 is a continuation of application No. 16/170,997, filed on Oct. 25, 2018, granted, now 10,949,648.
Claims priority of provisional application 62/620,823, filed on Jan. 23, 2018.
Prior Publication US 2023/0394681 A1, Dec. 7, 2023
Int. Cl. G06T 7/246 (2017.01); G06T 7/73 (2017.01); G06T 13/40 (2011.01); G06V 10/62 (2022.01); G06V 10/774 (2022.01); G06V 40/16 (2022.01)
CPC G06T 7/251 (2017.01) [G06T 7/73 (2017.01); G06T 13/40 (2013.01); G06V 10/774 (2022.01); G06V 40/161 (2022.01); G06V 40/165 (2022.01); G06V 40/176 (2022.01); G06V 10/62 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
identifying a facial area depicted within a set of images within a video stream;
extracting a dense motion flow of the facial area from the set of images;
mapping the dense motion flow of each facial vertex within a set of facial regions in the facial area;
identifying highly correlated points within the set of facial regions;
computing correlative values of correlation matrices between vertices of the facial area corresponding to the identified highly correlated points, the computing of the correlative values comprising generating normalized correlation matrices for each facial vertex and averaging the correlation matrices into an averaged overall correlation matrix; and
tracking a face across the set of images based on the set of facial regions, the correlative values between the vertices, and the dense motion flow mapped to the respective facial region in the set of facial regions.