Open AccessA Comparative Analysis of Feed-Forward and Generalized Regression Neural Networks for Face Recognition Using Principal Component Analysis
Author(s) -
Amit Kumar,
Mahesh Jangid Kartar Singh
Publication year - 2012
Publication title -
international journal of computer and technology
Language(s) - English
Resource type - Journals
ISSN - 2277-3061
DOI - 10.24297/ijct.v2i3c.2714
Subject(s) - principal component analysis , artificial neural network , pattern recognition (psychology) , artificial intelligence , computer science , facial recognition system , time delay neural network , feedforward neural network , classifier (uml) , feature extraction , hybrid neural network , face (sociological concept) , probabilistic neural network , speech recognition , social science , sociology
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.