REAL-TIME FACE RECOGNITION WITH ARTIFICIAL NEURAL NETWORK TRAINED BY PARTICLE SWARM OPTIMIZATION

PEKER, Musa and GURULER, Huseyin (2014) REAL-TIME FACE RECOGNITION WITH ARTIFICIAL NEURAL NETWORK TRAINED BY PARTICLE SWARM OPTIMIZATION. International Symposium on Sustainable Development. ISSN 978-9958-834-36-3

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Abstract

Face recognition is one of the widely used biometric method. Verification and recognition of individuals is possible via the features obtained from desired face image and compared with the facial image by various methods. Automatic face recognition which is a fundamental research area in the scope of pattern recognition, is applied in many civil, military and commercial areas for the purpose of authentication and identification. In this study a real-time face recognition system was developed. It is aimed that identification of individuals who entering any field observed with a camera. After detecting the important facial points, they are presented as input data to feed-forward neural network. Particle swarm optimization was used as learning algorithm in the network. As a result, a novel real-time face detection method, which provide high accuracy has been developed. Keywords: Real time face detection, pattern recognition, neural network, particle swarm optimization.

Item Type: Article
Subjects: Q Science > Q Science (General)
Q Science > QH Natural history > QH301 Biology
Q Science > QH Natural history > QH426 Genetics
Divisions: International Symposium on Sustainable Development
Depositing User: Mr. Edis Bulic
Date Deposited: 23 Jun 2014 13:44
Last Modified: 23 Jun 2014 13:44
URI: http://eprints.ibu.edu.ba/id/eprint/2526

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