Multi‐view facial action unit detection via deep feature enhancement

Abstract Multi‐view facial action unit (AU) analysis has been a challenging research topic due to multiple disturbing variables, including subject identity biases, variational facial action unit intensities, facial occlusions and non‐frontal head‐poses. A deep feature enhancement (DFE) framework is...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chuangao Tang, Cheng Lu, Wenming Zheng, Yuan Zong, Sunan Li
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ced6bbb8b8f949f39138f41bc36cdb20
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ced6bbb8b8f949f39138f41bc36cdb20
record_format	dspace
spelling	oai:doaj.org-article:ced6bbb8b8f949f39138f41bc36cdb202021-12-03T08:34:31ZMulti‐view facial action unit detection via deep feature enhancement1350-911X0013-519410.1049/ell2.12322https://doaj.org/article/ced6bbb8b8f949f39138f41bc36cdb202021-12-01T00:00:00Zhttps://doi.org/10.1049/ell2.12322https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Multi‐view facial action unit (AU) analysis has been a challenging research topic due to multiple disturbing variables, including subject identity biases, variational facial action unit intensities, facial occlusions and non‐frontal head‐poses. A deep feature enhancement (DFE) framework is presented to tackle some of these coupled complex disturbing variables for multi‐view facial action unit detection. The authors' DFE framework is a novel end‐to‐end three‐stage feature learning model with taking subject identity biases, dynamic facial changes and head‐pose into consideration. It contains three feature enhancement modules, including coarse‐grained local and holistic spatial feature learning (LHSF), spatio‐temporal feature learning (STF) and head‐pose feature disentanglement (FD). Experimental results show that the proposed method achieved state‐of‐the‐art recognition performance on the FERA2017 dataset. The code is released at http://aip.seu.edu.cn/cgtang/.Chuangao TangCheng LuWenming ZhengYuan ZongSunan LiWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 25, Pp 970-972 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Chuangao Tang Cheng Lu Wenming Zheng Yuan Zong Sunan Li Multi‐view facial action unit detection via deep feature enhancement
description	Abstract Multi‐view facial action unit (AU) analysis has been a challenging research topic due to multiple disturbing variables, including subject identity biases, variational facial action unit intensities, facial occlusions and non‐frontal head‐poses. A deep feature enhancement (DFE) framework is presented to tackle some of these coupled complex disturbing variables for multi‐view facial action unit detection. The authors' DFE framework is a novel end‐to‐end three‐stage feature learning model with taking subject identity biases, dynamic facial changes and head‐pose into consideration. It contains three feature enhancement modules, including coarse‐grained local and holistic spatial feature learning (LHSF), spatio‐temporal feature learning (STF) and head‐pose feature disentanglement (FD). Experimental results show that the proposed method achieved state‐of‐the‐art recognition performance on the FERA2017 dataset. The code is released at http://aip.seu.edu.cn/cgtang/.
format	article
author	Chuangao Tang Cheng Lu Wenming Zheng Yuan Zong Sunan Li
author_facet	Chuangao Tang Cheng Lu Wenming Zheng Yuan Zong Sunan Li
author_sort	Chuangao Tang
title	Multi‐view facial action unit detection via deep feature enhancement
title_short	Multi‐view facial action unit detection via deep feature enhancement
title_full	Multi‐view facial action unit detection via deep feature enhancement
title_fullStr	Multi‐view facial action unit detection via deep feature enhancement
title_full_unstemmed	Multi‐view facial action unit detection via deep feature enhancement
title_sort	multi‐view facial action unit detection via deep feature enhancement
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/ced6bbb8b8f949f39138f41bc36cdb20
work_keys_str_mv	AT chuangaotang multiviewfacialactionunitdetectionviadeepfeatureenhancement AT chenglu multiviewfacialactionunitdetectionviadeepfeatureenhancement AT wenmingzheng multiviewfacialactionunitdetectionviadeepfeatureenhancement AT yuanzong multiviewfacialactionunitdetectionviadeepfeatureenhancement AT sunanli multiviewfacialactionunitdetectionviadeepfeatureenhancement
_version_	1718373417361604608

Multi‐view facial action unit detection via deep feature enhancement

Ejemplares similares