A facial expression recognition method based on face texture feature fusion

Aiming at facial expression recognition, the recognition rate is not high due to noise and occlusion. A hybrid approach of facial expression has been presented by combining local and global features. First, feature extraction is performed to fuse the histogram of oriented gradients (HOG) descriptor...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tingting GAO, Hang LI, Shoulin YIN
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	pattern recognition; facial expression recognition; feature fusion; hog; c-ltp; support vector machine Technology T
Acceso en línea:	https://doaj.org/article/10f1567acea3403b8f0bc06efc57c887
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:10f1567acea3403b8f0bc06efc57c887
record_format	dspace
spelling	oai:doaj.org-article:10f1567acea3403b8f0bc06efc57c8872021-11-23T07:16:39ZA facial expression recognition method based on face texture feature fusion1008-154210.7535/hbkd.2021yx02004https://doaj.org/article/10f1567acea3403b8f0bc06efc57c8872021-04-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202102004&flag=1&journal_https://doaj.org/toc/1008-1542Aiming at facial expression recognition, the recognition rate is not high due to noise and occlusion. A hybrid approach of facial expression has been presented by combining local and global features. First, feature extraction is performed to fuse the histogram of oriented gradients (HOG) descriptor with the compounded local ternary pattern (C-LTP) descriptor. Second, features extracted by HOG and C-LTP are fused into a single feature vector. Third, the feature vector is sent to a multi-class support vector machine classifier for facial classification. Finally, the proposed method is compared with the existing facial expression recognition methods in three public facial expression image databases, and the results show that the recognition rates of the proposed method in MMI, JAFFE and CK[KG-*2]+ databases are 98.28%, 95.75% and 99.64%, respectively. The average recognition rate is 10% higher than other methods, which is better than other existing methods. The results of this study provide a reference for the research of facial expression recognition in many situations. The method of facial expression recognition proposed can effectively promote the development of human-computer interaction system and the study of computer image understanding. It is of great significance to realize the fusion of human language and natural language, as well as the establishment and implementation of the connection model between language and expression.Tingting GAOHang LIShoulin YINHebei University of Science and Technologyarticlepattern recognition; facial expression recognition; feature fusion; hog; c-ltp; support vector machineTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 2, Pp 119-126 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	pattern recognition; facial expression recognition; feature fusion; hog; c-ltp; support vector machine Technology T
spellingShingle	pattern recognition; facial expression recognition; feature fusion; hog; c-ltp; support vector machine Technology T Tingting GAO Hang LI Shoulin YIN A facial expression recognition method based on face texture feature fusion
description	Aiming at facial expression recognition, the recognition rate is not high due to noise and occlusion. A hybrid approach of facial expression has been presented by combining local and global features. First, feature extraction is performed to fuse the histogram of oriented gradients (HOG) descriptor with the compounded local ternary pattern (C-LTP) descriptor. Second, features extracted by HOG and C-LTP are fused into a single feature vector. Third, the feature vector is sent to a multi-class support vector machine classifier for facial classification. Finally, the proposed method is compared with the existing facial expression recognition methods in three public facial expression image databases, and the results show that the recognition rates of the proposed method in MMI, JAFFE and CK[KG-*2]+ databases are 98.28%, 95.75% and 99.64%, respectively. The average recognition rate is 10% higher than other methods, which is better than other existing methods. The results of this study provide a reference for the research of facial expression recognition in many situations. The method of facial expression recognition proposed can effectively promote the development of human-computer interaction system and the study of computer image understanding. It is of great significance to realize the fusion of human language and natural language, as well as the establishment and implementation of the connection model between language and expression.
format	article
author	Tingting GAO Hang LI Shoulin YIN
author_facet	Tingting GAO Hang LI Shoulin YIN
author_sort	Tingting GAO
title	A facial expression recognition method based on face texture feature fusion
title_short	A facial expression recognition method based on face texture feature fusion
title_full	A facial expression recognition method based on face texture feature fusion
title_fullStr	A facial expression recognition method based on face texture feature fusion
title_full_unstemmed	A facial expression recognition method based on face texture feature fusion
title_sort	facial expression recognition method based on face texture feature fusion
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/10f1567acea3403b8f0bc06efc57c887
work_keys_str_mv	AT tingtinggao afacialexpressionrecognitionmethodbasedonfacetexturefeaturefusion AT hangli afacialexpressionrecognitionmethodbasedonfacetexturefeaturefusion AT shoulinyin afacialexpressionrecognitionmethodbasedonfacetexturefeaturefusion AT tingtinggao facialexpressionrecognitionmethodbasedonfacetexturefeaturefusion AT hangli facialexpressionrecognitionmethodbasedonfacetexturefeaturefusion AT shoulinyin facialexpressionrecognitionmethodbasedonfacetexturefeaturefusion
_version_	1718416808150564864

A facial expression recognition method based on face texture feature fusion

Ejemplares similares