Development of Artificial Neural Network Architecture for Face Recognition in Real Time
| Main Authors: | Supardi, Julian, Syahrini Utami, Alvi |
|---|---|
| Format: | Article PeerReviewed application/pdf |
| Terbitan: |
IACSIT Press
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| Subjects: | |
| Online Access: |
http://eprints.unsri.ac.id/2911/1/Final__ICIIC_Julian%2DAlvi.pdf http://www.ilkom.unsri.ac.id http://eprints.unsri.ac.id/2911/ |
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2911 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title>Development of Artificial Neural Network Architecture for Face Recognition in Real Time</title><creator>Supardi, Julian</creator><creator>Syahrini Utami, Alvi</creator><subject>Computer Engineering</subject><description>Abstracts -- Face has a biological structure that is not simple. Nevertheless, research shows that some elements of the face have the geometric characteristics that can be measured. These characteristics are called face anthropometric. The existence of face anthropometric has provided significant clues for researchers to reduce the complexity of face recognition by computer. Although various methods have been developed to face recognition, but generally the system developed accepts input from a file. This condition is a one of face recognition system causes that has not been widely applied in real world. This paper presents a system that recognizes faces in real time. Artificial Neural Networks chosen as a tool for classification, to improve recognition accuracy. In this research, there are two Neural Networks used, radial basis neural network and Back-propagation neural network. The results obtained in this research shows that the accuracy of the ANN architecture that developed is still not well, which is 80%, but the Neural Network achieves convergence in 8-9 time of repetitions.</description><publisher>IACSIT Press</publisher><type>Journal:Article</type><type>PeerReview:PeerReviewed</type><type>File:application/pdf</type><identifier>http://eprints.unsri.ac.id/2911/1/Final__ICIIC_Julian%2DAlvi.pdf</identifier><relation>http://www.ilkom.unsri.ac.id</relation><identifier>Supardi, Julian and Syahrini Utami, Alvi Development of Artificial Neural Network Architecture for Face Recognition in Real Time. International Journal Machine Learning and Computing (IJMLC). ISSN 2010-3700 (In Press)</identifier><relation>http://eprints.unsri.ac.id/2911/</relation><recordID>2911</recordID></dc>
|
| format |
Journal:Article Journal PeerReview:PeerReviewed PeerReview File:application/pdf File |
| author |
Supardi, Julian Syahrini Utami, Alvi |
| title |
Development of Artificial Neural Network Architecture for Face Recognition in Real Time |
| publisher |
IACSIT Press |
| topic |
Computer Engineering |
| url |
http://eprints.unsri.ac.id/2911/1/Final__ICIIC_Julian%2DAlvi.pdf http://www.ilkom.unsri.ac.id http://eprints.unsri.ac.id/2911/ |
| contents |
Abstracts -- Face has a biological structure that is not simple. Nevertheless, research shows that some elements of the face have the geometric characteristics that can be measured. These characteristics are called face anthropometric. The existence of face anthropometric has provided significant clues for researchers to reduce the complexity of face recognition by computer. Although various methods have been developed to face recognition, but generally the system developed accepts input from a file. This condition is a one of face recognition system causes that has not been widely applied in real world. This paper presents a system that recognizes faces in real time. Artificial Neural Networks chosen as a tool for classification, to improve recognition accuracy. In this research, there are two Neural Networks used, radial basis neural network and Back-propagation neural network. The results obtained in this research shows that the accuracy of the ANN architecture that developed is still not well, which is 80%, but the Neural Network achieves convergence in 8-9 time of repetitions. |
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Universitas Sriwijaya |
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KOTA PALEMBANG |
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