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:
Mar. 09, 2021

Filed:

Dec. 31, 2017
Applicant:

Altumview Systems Inc., Burnaby, CA;

Inventors:

Zili Yi, Coquitlam, CA;

Xing Wang, Burnaby, CA;

Him Wai Ng, Coquitlam, CA;

Sami Ma, Burnaby, CA;

Jie Liang, Coquitlam, CA;

Assignee:

AltumView Systems Inc., Port Moody, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06K 9/46 (2006.01); G06N 5/02 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06K 9/00288 (2013.01); G06K 9/00228 (2013.01); G06K 9/00268 (2013.01); G06K 9/00926 (2013.01); G06K 9/4628 (2013.01); G06K 9/6212 (2013.01); G06K 9/6215 (2013.01); G06K 9/6256 (2013.01); G06K 9/6257 (2013.01); G06K 9/6262 (2013.01); G06K 9/6274 (2013.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01);
Abstract

Embodiments described herein provide various examples of a face-image training data preparation system for performing large-scale face-image training data acquisition, preprocessing, cleaning, balancing, and post-processing. The disclosed training data preparation system can collect a very large set of loosely-labeled images of different people from the public domain, and then generate a raw training dataset including a set of incorrectly-labeled face images. The disclosed training data preparation system can then perform cleaning and balancing operations on the raw training dataset to generate a high-quality face-image training dataset free of the incorrectly-labeled face images. The processed high-quality face-image training dataset can be subsequently used to train deep-neural-network-based face recognition systems to achieve high performance in various face recognition applications. Compared to conventional face recognition systems and techniques, the disclosed training data preparation system and technique provide a fully-automatic, highly-deterministic and high-quality training data preparation procedure which does not rely heavily on assumptions.


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