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: firstname.lastname@example.org).
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.