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:
Dec. 07, 2021

Filed:

Dec. 06, 2018
Applicant:

Beijing Kuangshi Technology Co., Ltd., Beijing, CN;

Inventors:

Jianghu Lu, Beijing, CN;

Peng Wang, Beijing, CN;

Cong Yao, Beijing, CN;

Yahao Sun, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/194 (2017.01); G06T 7/11 (2017.01); G06T 7/246 (2017.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00906 (2013.01); G06K 9/00718 (2013.01); G06K 9/6262 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06T 7/248 (2017.01); G06T 2207/10016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30196 (2013.01);
Abstract

A living body detection method and system, and a computer-readable storage medium are disclosed. The living body detection method includes: acquiring a video including an object to be detected; extracting at least two images to be detected from the video, and determining optical flow information according to the at least two images to be detected; dividing each image to be detected into a foreground image and a background image according to the optical flow information; using a classifier to perform category judgment on the foreground image and the background image to obtain a category distribution of the foreground image and a category distribution of the background image; and obtaining a probability that the object to be detected is a living body according to the category distribution of the foreground image and the category distribution of the background image.


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