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. 04, 2022

Filed:

Sep. 09, 2021
Applicant:

Institute of Automation, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Jianhua Tao, Beijing, CN;

Mingyuan Xiao, Beijing, CN;

Bin Liu, Beijing, CN;

Zheng Lian, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06K 9/00 (2006.01); G06T 5/00 (2006.01); G06T 5/40 (2006.01); G06K 9/46 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00302 (2013.01); G06K 9/00228 (2013.01); G06K 9/00281 (2013.01); G06K 9/00718 (2013.01); G06K 9/00744 (2013.01); G06K 9/00765 (2013.01); G06K 9/4647 (2013.01); G06K 9/629 (2013.01); G06K 9/6256 (2013.01); G06K 9/6269 (2013.01); G06N 3/04 (2013.01); G06T 5/009 (2013.01); G06T 5/40 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/30201 (2013.01);
Abstract

An expression recognition method under a natural scene comprises: converting an input video into a video frame sequence in terms of a specified frame rate, and performing facial expression labeling on the video frame sequence to obtain a video frame labeled sequence; removing natural light impact, non-face areas, and head posture impact elimination on facial expression from the video frame labeled sequence to obtain an expression video frame sequence; augmenting the expression video frame sequence to obtain a video preprocessed frame sequence; from the video preprocessed frame sequence, extracting HOG features that characterize facial appearance and shape features, extracting second-order features that describe a face creasing degree, and extracting facial pixel-level deep neural network features by using a deep neural network; then, performing vector fusion on these three obtain facial feature fusion vectors for training; and inputting the facial feature fusion vectors into a support vector machine for expression classification.


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