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Illumination and Expression Invariant Automatic Human Face Recognition using Wavelet, Eigen and Fisher Analysis

by Shubhankar De, Ranjan Parekh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 21
Year of Publication: 2015
Authors: Shubhankar De, Ranjan Parekh
10.5120/20681-3528

Shubhankar De, Ranjan Parekh . Illumination and Expression Invariant Automatic Human Face Recognition using Wavelet, Eigen and Fisher Analysis. International Journal of Computer Applications. 117, 21 ( May 2015), 28-35. DOI=10.5120/20681-3528

@article{ 10.5120/20681-3528,
author = { Shubhankar De, Ranjan Parekh },
title = { Illumination and Expression Invariant Automatic Human Face Recognition using Wavelet, Eigen and Fisher Analysis },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 21 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number21/20681-3528/ },
doi = { 10.5120/20681-3528 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:02.319280+05:30
%A Shubhankar De
%A Ranjan Parekh
%T Illumination and Expression Invariant Automatic Human Face Recognition using Wavelet, Eigen and Fisher Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 21
%P 28-35
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper studies recognition of human faces using wavelet transform, Eigen space mapping and Linear Discriminant Analysis/Fisher Analysis (LDA). Histogram Equalization is chosen as a preprocessing step to reduce the effect of variation in illumination on human faces. The preprocessed faces are then subjected to second level wavelet (Haar) decomposition for further calculation. Feature extraction is performed using Eigen space mapping followed by LDA on the second level approximation matrix (LL sub band). Manhattan distance is used as a classifier. The proposed scheme is tested on illumination and expression variant different face databases for performance evaluation.

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Index Terms

Computer Science
Information Sciences

Keywords

Histogram Equalization Wavelet Transform Principle Component Analysis (PCA) Linear Discriminant Analysis (LDA)/Fisher Analysis

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