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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Online Access: | http://dx.doi.org/10.1155/2014/879031 |
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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 |
work_keys_str_mv |
AT mohammedhasanabdulameer amodifiedactiveappearancemodelbasedonanadaptiveartificialbeecolony AT sitinorulhudasheikhabdullah amodifiedactiveappearancemodelbasedonanadaptiveartificialbeecolony AT zulaihaaliothman amodifiedactiveappearancemodelbasedonanadaptiveartificialbeecolony AT mohammedhasanabdulameer modifiedactiveappearancemodelbasedonanadaptiveartificialbeecolony AT sitinorulhudasheikhabdullah modifiedactiveappearancemodelbasedonanadaptiveartificialbeecolony AT zulaihaaliothman modifiedactiveappearancemodelbasedonanadaptiveartificialbeecolony |
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