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:
Oct. 06, 2020

Filed:

Sep. 04, 2018
Applicant:

Kaikutek Inc., Taipei, TW;

Inventors:

Tsung-Ming Tai, New Taipei, TW;

Yun-Jie Jhang, Taoyuan, TW;

Wen-Jyi Hwang, Taipei, TW;

Chun-Hsuan Kuo, San Diego, CA (US);

Assignee:

KAIKUTEK INC., Taipei, TW;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G01S 13/89 (2006.01); G01S 7/41 (2006.01); G06K 9/20 (2006.01); G06K 9/46 (2006.01); G06N 20/00 (2019.01); G06F 3/01 (2006.01); G06F 9/50 (2006.01); G06N 3/08 (2006.01); H03B 21/02 (2006.01); G01S 13/58 (2006.01); G06T 7/20 (2017.01); G06F 17/18 (2006.01); G01S 13/50 (2006.01); G01S 7/35 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00335 (2013.01); G01S 7/414 (2013.01); G01S 7/417 (2013.01); G01S 13/584 (2013.01); G01S 13/89 (2013.01); G06F 3/017 (2013.01); G06F 9/5027 (2013.01); G06F 17/18 (2013.01); G06K 9/2018 (2013.01); G06K 9/4628 (2013.01); G06K 9/6215 (2013.01); G06K 9/6256 (2013.01); G06K 9/6259 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06K 9/6271 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/20 (2013.01); H03B 21/02 (2013.01); G01S 7/415 (2013.01); G01S 13/50 (2013.01); G01S 2007/356 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A gesture recognition system using siamese neural network executes a gesture recognition method. The gesture recognition method includes steps of: receiving a first training signal to calculate a first feature; receiving a second training signal to calculate a second feature; determining a distance between the first feature and the second feature in a feature space; adjusting the distance between the first feature and the second feature in feature space according to a predetermined parameter. Two neural networks are used to generate the first feature and the second feature, and determine the distance between the first feature and the second feature in the feature space for training the neural networks. Therefore, the gesture recognition system does not need a big amount of data to train one neural network for classifying a sensing signal. A user may easily define a new personalized gesture.


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