Sistem Kontrol Akses Berbasis Real Time Face Recognition dan Gender Information

Authors

  • Putri Nurmala Bina Nusantara University
  • Wikaria Gazali Bina Nusantara University
  • Widodo Budiharto Bina Nusantara University

DOI:

https://doi.org/10.21512/comtech.v6i2.2264

Keywords:

face recognition, gender information, real time, PCA, Arduino

Abstract

Face recognition and gender information is a computer application for automatically identifying or verifying a person's face from a camera to capture a person's face. It is usually used in access control systems
and it can be compared to other biometrics such as finger print identification system or iris. Many of face recognition algorithms have been developed in recent years. Face recognition system and gender information in
this system based on the Principal Component Analysis method (PCA). Computational method has a simple and fast compared with the use of the method requires a lot of learning, such as artificial neural network. In this
access control system, relay used and Arduino controller. In this essay focuses on face recognition and gender - based information in real time using the method of Principal Component Analysis ( PCA ). The result achieved
from the application design is the identification of a person’s face with gender using PCA. The results achieved by the application is face recognition system using PCA can obtain good results the 85 % success rate in face recognition with face images that have been tested by a few people and a fairly high degree of accuracy.

Dimensions

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References

Budiharto, W. (2014). Modern Robotics with OpenCV. Jakarta: Science Publishing Group.

Budiharto, W. (2014). The Access Control System Based On Linear Discriminant Analysis. Journal of Computer Science, 10(3), 453-457.

Eleyan, A., Demirel, H. (2007). PCA and LDA Based Neural Networks for Human Face Recognition. Face Recognition. Austria: Intech.

Pressman, R. S. (2010). Software Engineering: A Practitioner’s Approach 7th Edition. New York: McGraw-Hill Higher Education.

Sommerville, I. (2011). Software Engineering (9th ed.). Pearson.

Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Washington: Springer.

Turk, M. and Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3.

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Published

2015-06-01

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Articles
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