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Biometric face classification with the hybridised rough neural network

Biometric face classification with the hybridised rough neural network Biometric face classification is an important indexing scheme to reduce face matching time for large volumes of a database. In this paper, a hybridised approach based on rough set theory (RST) and back propagation neural network (BPN) to classify human face is proposed. Local binary pattern (LBP) method is exploited to extract the features from pre-processed face images. The evolutionary optimisation algorithms such as genetic algorithm (GA), particle swarm optimisation (PSO), ant colony optimisation (ACO), hybridisation of ACO and GA (ACO-GA) and hybridisation of PSO and GA (PSO-GA) are investigated for feature selection. Finally, the hybridised rough neural network (RNN) is employed for classification. The experimental results of the proposed RNN is compared in terms of precision, recall, f-measure, accuracy and error rate with Naive Bayes, support vector machine (SVM), radial basis function network (RBFN), conventional BPN, and convolutional neural network (CNN) to conclude the efficacy of the proposed approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Biometric face classification with the hybridised rough neural network

Sasirekha, K.; Thangavel, K.
International Journal of Biometrics , Volume 12 (2): 25 – Jan 1, 2020
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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2020.107717
Publisher site
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Abstract

Biometric face classification is an important indexing scheme to reduce face matching time for large volumes of a database. In this paper, a hybridised approach based on rough set theory (RST) and back propagation neural network (BPN) to classify human face is proposed. Local binary pattern (LBP) method is exploited to extract the features from pre-processed face images. The evolutionary optimisation algorithms such as genetic algorithm (GA), particle swarm optimisation (PSO), ant colony optimisation (ACO), hybridisation of ACO and GA (ACO-GA) and hybridisation of PSO and GA (PSO-GA) are investigated for feature selection. Finally, the hybridised rough neural network (RNN) is employed for classification. The experimental results of the proposed RNN is compared in terms of precision, recall, f-measure, accuracy and error rate with Naive Bayes, support vector machine (SVM), radial basis function network (RBFN), conventional BPN, and convolutional neural network (CNN) to conclude the efficacy of the proposed approach.

Journal

International Journal of Biometrics – Inderscience Publishers

Published: Jan 1, 2020

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