A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with orig...

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Main Authors: Mohammed Hasan Abdulameer, Siti Norul Huda Sheikh Abdullah, Zulaiha Ali Othman
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/879031
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spelling doaj-9a17e09211a84f81805804f589e611ea2020-11-25T01:17:56ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/879031879031A Modified Active Appearance Model Based on an Adaptive Artificial Bee ColonyMohammed Hasan Abdulameer0Siti Norul Huda Sheikh Abdullah1Zulaiha Ali Othman2Pattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, MalaysiaPattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, MalaysiaData Mining and Optimization Group, Faculty of Information System and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, MalaysiaActive appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.http://dx.doi.org/10.1155/2014/879031
collection DOAJ
language English
format Article
sources DOAJ
author Mohammed Hasan Abdulameer
Siti Norul Huda Sheikh Abdullah
Zulaiha Ali Othman
spellingShingle Mohammed Hasan Abdulameer
Siti Norul Huda Sheikh Abdullah
Zulaiha Ali Othman
A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
The Scientific World Journal
author_facet Mohammed Hasan Abdulameer
Siti Norul Huda Sheikh Abdullah
Zulaiha Ali Othman
author_sort Mohammed Hasan Abdulameer
title A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
title_short A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
title_full A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
title_fullStr A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
title_full_unstemmed A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
title_sort modified active appearance model based on an adaptive artificial bee colony
publisher Hindawi Limited
series The Scientific World Journal
issn 2356-6140
1537-744X
publishDate 2014-01-01
description Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.
url http://dx.doi.org/10.1155/2014/879031
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