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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(1): 110-113 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.396

Development of Artificial Neural Network Architecture for Face Recognition in Real Time

Julian Supardi and Alvi Syahrini Utami
Abstract—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 backpropagation 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.

Index Terms—Artificial neural network, face recognition, backpropagation, radial base function.

The authors are with Informatics Engineering Department, Computer Science Faculty, Sriwijaya University, Indonesia (e-mail: julianazdin@gmail.com).


Cite:Julian Supardi and Alvi Syahrini Utami, "Development of Artificial Neural Network Architecture for Face Recognition in Real Time," International Journal of Machine Learning and Computing vol.4, no. 1, pp. 110-113, 2014.

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