A Comparative Analysis of Feed-Forward and Generalized Regression Neural Networks for Face Recognition Using Principal Component Analysis

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

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Field Value
 
Title A Comparative Analysis of Feed-Forward and Generalized Regression Neural Networks for Face Recognition Using Principal Component Analysis
 
Creator Kumar, Amit
Singh, Mr. Mahesh
 
Subject Computer Science, Information Technology, electronics etc.
Feed forward neural network, Generalized regression neural network, Principal Component Analysis.
 
Description In this paper we give a comparative analysis of performance of feed forward neural network and generalized regression neural network based face recognition. We use different inner epoch for different input pattern according to their difficulty of recognition. We run our system for different number of training patterns and test the system’s performance in terms of recognition rate and training time. We run our algorithm for face recognition application using Principal Component Analysis and both neural network. PCA is used for feature extraction and the neural network is used as a classifier to identify the faces. We use the ORL database for all the experiments.
 
Publisher CIRWORLD
 
Date 2003-12-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://cirworld.com/index.php/ijct/article/view/2714
 
Source INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY; Vol 2 No 3c; 148-154
2277-3061
 
Language eng
 
Relation http://cirworld.com/index.php/ijct/article/view/2714/2638
 
Rights Copyright (c) 2016 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
http://creativecommons.org/licenses/by/4.0
 

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