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:
Jan. 10, 2023

Filed:

Mar. 30, 2021
Applicant:

Beijing Xiaomi Pinecone Electronics Co., Ltd., Beijing, CN;

Inventors:

Zhi Qu, Beijing, CN;

Yasen Zhang, Beijing, CN;

Yan Song, Beijing, CN;

Zhipeng Ge, Beijing, CN;

Ruoyu Liu, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 40/10 (2022.01); G06T 7/73 (2017.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 11/60 (2006.01); H04N 5/232 (2006.01);
U.S. Cl.
CPC ...
G06V 40/11 (2022.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 7/74 (2017.01); G06T 11/60 (2013.01); H04N 5/23222 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30196 (2013.01);
Abstract

A method for detecting a finger occlusion image includes: N first original occlusion images and M first non-occlusion images are acquired, and first training data set is generated based on first original occlusion images and first non-occlusion images; first training is performed, based on first training data set, on neural network model for detection of finger occlusion image; L second original occlusion images and K second non-occlusion images are acquired, and second training data set is generated based on second original occlusion images and second non-occlusion images; linear classifier in neural network model having completed first training is replaced with iterative training module to form finger occlusion image detection model; second training is performed on finger occlusion image detection model based on second training data set; image to be detected is input into trained finger occlusion image detection model, to determine whether image to be detected is finger occlusion image.


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