Robust face recognition against expressions and partial occlusions

Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features’ contribution ac...

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Main Authors:	Hafiz, Fadhlan Kamaru Zaman, Shafie, Amir Akramin, Mohd. Mustafah, Yasir
Format:	Article
Language:	English English
Published:	Chinese Academy of Sciences 2016
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://irep.iium.edu.my/56447/ http://irep.iium.edu.my/56447/ http://irep.iium.edu.my/56447/ http://irep.iium.edu.my/56447/1/56447_Robust%20face%20recognition.pdf http://irep.iium.edu.my/56447/2/56447_Robust%20face%20recognition_SCOPUS.pdf

id	iium-56447
recordtype	eprints
spelling	iium-564472017-07-03T02:35:12Z http://irep.iium.edu.my/56447/ Robust face recognition against expressions and partial occlusions Hafiz, Fadhlan Kamaru Zaman Shafie, Amir Akramin Mohd. Mustafah, Yasir TA Engineering (General). Civil engineering (General) Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features’ contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis (ICA) features. These local features are acquired using locally lateral subspace (LLS) strategy. Then, through linear discriminant analysis (LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature’s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding (LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK+ is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects. © 2016, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg Chinese Academy of Sciences 2016-08-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/56447/1/56447_Robust%20face%20recognition.pdf application/pdf en http://irep.iium.edu.my/56447/2/56447_Robust%20face%20recognition_SCOPUS.pdf Hafiz, Fadhlan Kamaru Zaman and Shafie, Amir Akramin and Mohd. Mustafah, Yasir (2016) Robust face recognition against expressions and partial occlusions. International Journal of Automation and Computing, 13 (4). pp. 319-337. ISSN 1476-8186 https://link.springer.com/article/10.1007/s11633-016-0974-6 10.1007/s11633-016-0974-6
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Hafiz, Fadhlan Kamaru Zaman Shafie, Amir Akramin Mohd. Mustafah, Yasir Robust face recognition against expressions and partial occlusions
description	Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features’ contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis (ICA) features. These local features are acquired using locally lateral subspace (LLS) strategy. Then, through linear discriminant analysis (LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature’s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding (LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK+ is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects. © 2016, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
format	Article
author	Hafiz, Fadhlan Kamaru Zaman Shafie, Amir Akramin Mohd. Mustafah, Yasir
author_facet	Hafiz, Fadhlan Kamaru Zaman Shafie, Amir Akramin Mohd. Mustafah, Yasir
author_sort	Hafiz, Fadhlan Kamaru Zaman
title	Robust face recognition against expressions and partial occlusions
title_short	Robust face recognition against expressions and partial occlusions
title_full	Robust face recognition against expressions and partial occlusions
title_fullStr	Robust face recognition against expressions and partial occlusions
title_full_unstemmed	Robust face recognition against expressions and partial occlusions
title_sort	robust face recognition against expressions and partial occlusions
publisher	Chinese Academy of Sciences
publishDate	2016
url	http://irep.iium.edu.my/56447/ http://irep.iium.edu.my/56447/ http://irep.iium.edu.my/56447/ http://irep.iium.edu.my/56447/1/56447_Robust%20face%20recognition.pdf http://irep.iium.edu.my/56447/2/56447_Robust%20face%20recognition_SCOPUS.pdf
first_indexed	2023-09-18T21:19:38Z
last_indexed	2023-09-18T21:19:38Z
_version_	1777411795422543872

Robust face recognition against expressions and partial occlusions

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