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An Efficient Face Recognition with ANN using Hybrid Feature Extraction Methods

by Mithila Sompura, Vinit Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 17
Year of Publication: 2015
Authors: Mithila Sompura, Vinit Gupta
10.5120/20647-3405

Mithila Sompura, Vinit Gupta . An Efficient Face Recognition with ANN using Hybrid Feature Extraction Methods. International Journal of Computer Applications. 117, 17 ( May 2015), 19-23. DOI=10.5120/20647-3405

@article{ 10.5120/20647-3405,
author = { Mithila Sompura, Vinit Gupta },
title = { An Efficient Face Recognition with ANN using Hybrid Feature Extraction Methods },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 17 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number17/20647-3405/ },
doi = { 10.5120/20647-3405 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:12.582078+05:30
%A Mithila Sompura
%A Vinit Gupta
%T An Efficient Face Recognition with ANN using Hybrid Feature Extraction Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 17
%P 19-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day's face recognition is the most interesting and active research area in the field of phycology, neuroscience, and computer vision. In this paper a fast efficient algorithm is developed with the better recognition rate of face in different conditions that is illumination, head pose, expressions etc. In which we have extract the global and local features using PCA (Principle Component Analysis) and LBP (Local Binary Pattern) respectively. we have experiment the proposed algorithm with the standard Yale database which contains 15 individuals with each contains 11 images(15*11). So the fusion of Global and Local features are fed to the MLP (Multilayer Perceptron). The BPMLP (Backpropagation Multilayer Perceptron) is used for the classification. The proposed method achieves 93% accuracy compare to the existing approach.

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Index Terms

Computer Science
Information Sciences

Keywords

PCA (Principle Component Analysis) LDA (Linear Discriminant Analysis) LBP (Local Binary Pattern) BPMLP (Backpropagation Multilayer perceptron) k-NN (k- nearest neighbor) ICA (Independent Component Analysis) KSOM (Kohonen Self organizing Map) LPP (Linear Parallel Projection)

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