dc.contributor.author | Almabdy, Soad | |
dc.contributor.author | Elrefaei, Lamiaa | |
dc.date.accessioned | 2020-07-21T09:08:49Z | |
dc.date.available | 2020-07-21T09:08:49Z | |
dc.date.issued | 2021-04-01 | |
dc.identifier.issn | 2210-142X | |
dc.identifier.uri | https://journal.uob.edu.bh:443/handle/123456789/3990 | |
dc.description.abstract | Recently, face recognition applications achieved promising results by using Convolutional Neural Network (CNN). CNN has the capability to extract features automatically from images and does not need to extract hand-crafted features as traditional algorithms. Feature fusion aims to provide improvements of data validity for both traditional algorithms and deep learning algorithms. In this paper we propose a feature fusion approach for face recognition, the approach performs fusion at the feature level by applying two pre-trained CNNs AlexNet and ResNet-50. Firstly, extracting the feature from both pre-trained CNN AlexNet and ResNet-50 separately. Secondly, fuse the feature maps learned from AlexNet and ResNet-50. Finally, a Support Vector Machine (SVM) classier is used for the classification task. Experiments are conducted on the following datasets: FEI face, GTAV face, ORL, F_LFW, Georgia Tec Face, LFW, DB_Collection, demonstrate the effectiveness of the proposed approach. In addition, the fusion of the two CNN based models AlexNet and ResNet-50 lead to significant performance improvement. In particular, the fusion approach achieves accuracy in range (96.21%-100%) on all datasets. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Bahrain | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Convolutional Neural Network (CNN) | en_US |
dc.subject | Face Recognition | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Feature Level Fusion | en_US |
dc.subject | Deep learning | en_US |
dc.title | Feature Extraction and Fusion for Face Recognition Systems using Pre-Trained Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.12785/ijcds/100144 | |
dc.volume | 9 | en_US |
dc.pagestart | 1 | en_US |
dc.pageend | 7 | en_US |
dc.source.title | International Journal of Computing and Digital Systems | en_US |
dc.abbreviatedsourcetitle | IJCDS | en_US |
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