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Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF

by T. Kathirvalavakumar, J. Jebakumari Beulah Vasanthi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 5
Year of Publication: 2013
Authors: T. Kathirvalavakumar, J. Jebakumari Beulah Vasanthi
10.5120/12884-9796

T. Kathirvalavakumar, J. Jebakumari Beulah Vasanthi . Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF. International Journal of Computer Applications. 74, 5 ( July 2013), 40-46. DOI=10.5120/12884-9796

@article{ 10.5120/12884-9796,
author = { T. Kathirvalavakumar, J. Jebakumari Beulah Vasanthi },
title = { Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 5 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number5/12884-9796/ },
doi = { 10.5120/12884-9796 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:27.843967+05:30
%A T. Kathirvalavakumar
%A J. Jebakumari Beulah Vasanthi
%T Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 5
%P 40-46
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognizing patterns by radial basis function network using reduced features obtained through wavelet transformation and discriminative common vector is proposed. Wavelet coefficients obtained after applying wavelet transformations on input patterns, are used to extract significant features from the samples. The discriminative common vectors are extracted using the within-class scatter matrix method from the wavelet coefficients. The discriminative common vectors are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. The proposed method reduces the number of features, minimizes the computational complexity and provides better recognition rates.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction Wavelet transformation Discriminative common vector Radial basis function network

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