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An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System

by Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 14
Year of Publication: 2015
Authors: Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam
10.5120/19605-1458

Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam . An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System. International Journal of Computer Applications. 111, 14 ( February 2015), 14-23. DOI=10.5120/19605-1458

@article{ 10.5120/19605-1458,
author = { Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam },
title = { An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 14 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number14/19605-1458/ },
doi = { 10.5120/19605-1458 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:52.166344+05:30
%A Yasmin Nabil Mousa
%A Abd - Al- Halim Zekry
%A Nariman Abd - Alsalam
%T An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 14
%P 14-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With fast evolving technology, it is necessary to design an efficient security system which can detect unauthorized access on any system. It's needed to implement an extremely secure, economic and perfect system for face recognition that can protect systems from unauthorized access. So, in this paper, a robust face recognition system approach is proposed for feature extraction using Principal Component Analysis, and recognition using Feed Forward Back Propagation Neural Network. The proposed approach gave better results in all aspects including recognition rate, training time, elapsed time and mean square error. The paper shows that when using 80% of the dataset for training, the proposed system can achieve up to 97. 5% recognition rate if correct number of input principal features to the classifier, learning rate, momentum, and number of hidden neurons are used. This algorithm is implemented using Matlab software, and tolerable error value used is superiorly chosen as MSE= 0. 0004.

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Index Terms

Computer Science
Information Sciences

Keywords

Face recognition Principal component analysis (PCA) Eigenvector Eigenface Artificial Neural network (ANN).

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