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Invariant face recognition using Zernike moments combined with feed forward neural network

Invariant face recognition using Zernike moments combined with feed forward neural network The paper proposes a face recognition system using Zernike moments (ZM) and feed forward neural network as a classifier. Magnitudes of the ZM, which are invariant to rotation, are used as feature vectors for efficient representation of the images. The experiment was conducted on the ORL and Texas 3D Face Recognition Database which has both colour and range images. The recognition performance with measures like overall recognition accuracy, false acceptance rate, false rejection rate and true rejection rate was evaluated with multilayer perceptron neural network, radial basis function neural network and probabilistic neural network for variable lengths of the feature vector using confusion matrix. The simulation results indicates that the invariant ZM with neural network classifier was successful in recognising the images constrained to different variations and illumination conditions. The overall classification accuracy of 99.7% with MLPNN and 99.6% with MLPNN was achieved with range images and grey images from Texas 3D Face Recognition Database, respectively. Furthermore, 99.5% accuracy with RBFNN was achieved from ORL database. Keywords: Zernike moments; multilayer perceptron neural network; MLPNN; radial basis function neural network; RBFNN; probabilistic neural network; PNN; face recognition; confusion matrix; accuracy; false acceptance rate; FAR; false rejection rate; FRR; true http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Invariant face recognition using Zernike moments combined with feed forward neural network

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2015.071950
Publisher site
See Article on Publisher Site

Abstract

The paper proposes a face recognition system using Zernike moments (ZM) and feed forward neural network as a classifier. Magnitudes of the ZM, which are invariant to rotation, are used as feature vectors for efficient representation of the images. The experiment was conducted on the ORL and Texas 3D Face Recognition Database which has both colour and range images. The recognition performance with measures like overall recognition accuracy, false acceptance rate, false rejection rate and true rejection rate was evaluated with multilayer perceptron neural network, radial basis function neural network and probabilistic neural network for variable lengths of the feature vector using confusion matrix. The simulation results indicates that the invariant ZM with neural network classifier was successful in recognising the images constrained to different variations and illumination conditions. The overall classification accuracy of 99.7% with MLPNN and 99.6% with MLPNN was achieved with range images and grey images from Texas 3D Face Recognition Database, respectively. Furthermore, 99.5% accuracy with RBFNN was achieved from ORL database. Keywords: Zernike moments; multilayer perceptron neural network; MLPNN; radial basis function neural network; RBFNN; probabilistic neural network; PNN; face recognition; confusion matrix; accuracy; false acceptance rate; FAR; false rejection rate; FRR; true

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2015

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