Texture feature extraction and optimization of facial expression based on weakly supervised clustering

In order to improve the recognition rate of weak annotation data in facial expression recognition task, this paper proposes a multi-scale and multi-region vector triangle texture feature extraction scheme based on weakly supervised clustering algorithm. According to the information gain rate of extr...

Full description

Saved in:
Bibliographic Details
Published in:Systems science & control engineering 2021-01, Vol.9 (1), p.514-528
Main Authors: Jiaming, Tang, Jiafa, Mao, Weiguo, Sheng, Yahong, Hu, Hua, Gao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Annotations
Classification
Clustering
Facial expression recognition
Feature extraction
feature optimization
Feature selection
Labels
Methods
Open access publishing
Optimization
Redundancy
Systems science
texture feature
Texture recognition
Wavelet transforms
weakly supervised clustering
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to improve the recognition rate of weak annotation data in facial expression recognition task, this paper proposes a multi-scale and multi-region vector triangle texture feature extraction scheme based on weakly supervised clustering algorithm. According to the information gain rate of extracted features, combined with threshold selection and random dropout strategy, the best selection of vector triangle texture feature scale is explored, and the feature space is optimized under the premise of sufficient feature space information, the reduction of feature space is realized and the information redundancy is reduced. For the positive and negative expression units, the facial expression images in the data set are divided into two categories. The positive and negative facial expressions are taken to form the same kind of samples, the positive and negative facial expressions are taken to form the positive and negative samples, and the annotation labels are taken to form the weak annotation labels. The experimental results show that the best recognition rate of the proposed scheme is 84.1%, which is 5.8% higher than the unoptimized texture feature scheme.
ISSN:2164-2583
2164-2583
DOI:10.1080/21642583.2021.1943725