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Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, featur...

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Main Authors:	Iqtait, M., Mohamad, F.S., Mamat, M.
Format:	Conference or Workshop Item
Language:	English English
Published:	2018
Subjects:	QA Mathematics QA75 Electronic computers. Computer science T Technology (General)
Online Access:	http://eprints.unisza.edu.my/1710/1/FH03-FIK-18-13687.jpg http://eprints.unisza.edu.my/1710/2/FH03-FIK-19-23934.pdf http://eprints.unisza.edu.my/1710/
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id	my-unisza-ir.1710
record_format	eprints
spelling	my-unisza-ir.17102020-11-22T02:40:16Z http://eprints.unisza.edu.my/1710/ Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM) Iqtait, M. Mohamad, F.S. Mamat, M. QA Mathematics QA75 Electronic computers. Computer science T Technology (General) Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture. 2018 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/1710/1/FH03-FIK-18-13687.jpg text en http://eprints.unisza.edu.my/1710/2/FH03-FIK-19-23934.pdf Iqtait, M. and Mohamad, F.S. and Mamat, M. (2018) Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM). In: International Conference on Operations Research of the Indonesian-Operations-Research-Association (IORA), 12 Oct 2017, Tangerang Selatan, Indonesia.
institution	Universiti Sultan Zainal Abidin
building	UNISZA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Sultan Zainal Abidin
content_source	UNISZA Institutional Repository
url_provider	https://eprints.unisza.edu.my/
language	English English
topic	QA Mathematics QA75 Electronic computers. Computer science T Technology (General)
spellingShingle	QA Mathematics QA75 Electronic computers. Computer science T Technology (General) Iqtait, M. Mohamad, F.S. Mamat, M. Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
description	Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.
format	Conference or Workshop Item
author	Iqtait, M. Mohamad, F.S. Mamat, M.
author_facet	Iqtait, M. Mohamad, F.S. Mamat, M.
author_sort	Iqtait, M.
title	Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
title_short	Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
title_full	Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
title_fullStr	Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
title_full_unstemmed	Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
title_sort	feature extraction for face recognition via active shape model (asm) and active appearance model (aam)
publishDate	2018
url	http://eprints.unisza.edu.my/1710/1/FH03-FIK-18-13687.jpg http://eprints.unisza.edu.my/1710/2/FH03-FIK-19-23934.pdf http://eprints.unisza.edu.my/1710/
_version_	1684657741093666816
score	13.244404

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

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