|Table of Contents|

Age Operator Deep Sparse Fusion Extension of the Expression Recognition(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年03期
Page:
43-52
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Age Operator Deep Sparse Fusion Extension of the Expression Recognition
Author(s):
Chen Bin; Fan Feiyan; Zhang Rui
(Informatization Office, Nanjing Normal University, Nanjing 210023, China)
Keywords:
expression recognition;  deep sparse;  fusion extension;  age operator;  machine learning;  convolutional neural network
PACS:
TP39
DOI:
10.3969/j.issn.1672-1292.2023.03.006
Abstract:
In order to solve the trait differences in expression characteristics of people at different ages, the age operator deep sparse fusion extended expression recognition model is proposed. The model uses the form of linear dictionary sequence as sparse input signal, the input object with linear combination of training set, and select the optimal solution, further by convolutional neural network convolution, pooling and full connection processing, through fusion extension strategy to solve the problem of data set similarity, insufficient quantity and uneven distribution, and on the basis of combining age operator as characteristic elements and expression features extraction and as the basis of classification decision. Through deep sparse fusion extension, the age operator anthropometric model, internal angle structure and skin wrinkles detection as expression feature extraction additional factors. The multiple data set experiment results prove the effectiveness and stability of the algorithm across data sets, and the representative across age expression classification algorithm transverse comparison proves the advantages of the algorithm. This method has good effects on the accuracy and robustness of cross-age expression recognition, and has certain research value and reference significance.

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Last Update: 2023-09-15