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:
Nov. 03, 2015

Filed:

Aug. 26, 2014
Applicant:

Tcl Research America Inc., San Jose, CA (US);

Inventors:

Liang Peng, San Jose, CA (US);

Yimin Yang, San Jose, CA (US);

Haohong Wang, San Jose, CA (US);

Assignee:

TCL RESEARCH AMERICA INC., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06F 17/30 (2006.01); G06K 9/62 (2006.01); G06T 5/00 (2006.01); G06F 17/24 (2006.01);
U.S. Cl.
CPC ...
G06F 17/30253 (2013.01); G06F 17/241 (2013.01); G06F 17/30289 (2013.01); G06F 17/30864 (2013.01); G06K 9/00228 (2013.01); G06K 9/6269 (2013.01); G06T 5/002 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/30201 (2013.01);
Abstract

An automatic face annotation method is provided. The method includes dividing an input video into different sets of frames, extracting temporal and spatial information by employing camera take and shot boundary detection algorithms on the different sets of frames, and collecting weakly labeled data by crawling weakly labeled face images from social networks. The method also includes applying face detection together with an iterative refinement clustering algorithm to remove noise of the collected weakly labeled data, generating a labeled database containing refined labeled images, finding and labeling exact frames containing one or more face images in the input video matching any of the refined labeled images based on the labeled database, labeling remaining unlabeled face tracks in the input video by a semi-supervised learning algorithm to annotate the face images in the input video, and outputting the input video containing the annotated face images.


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