An efficient face recognition system using eigen values of wavelet transform as feature vectors and radial basis function (RBF) neural network as classifier is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet transformation.The wavelet coefficients obtained from the wavelet transformation are averaged for finding centers of features. In train process, four output of wavelet transform is analyzed and all eigenvalues of these images is obtained. At next step, the maximum 10 eigenvalues of wavelet sub images is stored as feature. Based on four sub images of wavelet transform and 10 eigenvalues of each sub image, the length of feature vector is 40. After obtaining features, in the train process for each person a center that has minimum Euclidean distance from all features is selected using RBF function. In fact the features are recognized by a RBF network. For a new input face image, firstly the feature vector is computed and then the distance (error) of this new vector with all centers of all persons is checked. The minimum distance is selected as target face. The proposed method on Essex face database and resultsshowed that the proposed method provide better recognition rates with low computational complexity.
| Published in | American Journal of Networks and Communications (Volume 4, Issue 4) |
| DOI | 10.11648/j.ajnc.20150404.12 |
| Page(s) | 90-94 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Face Recognition, Singular Value Decomposition, SVD, Wavelet, Radial Basis Function, Neural Network
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APA Style
Vahid Haji Hashemi, Abdorreza Alavi Gharahbagh. (2015). A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition. American Journal of Networks and Communications, 4(4), 90-94. https://doi.org/10.11648/j.ajnc.20150404.12
ACS Style
Vahid Haji Hashemi; Abdorreza Alavi Gharahbagh. A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition. Am. J. Netw. Commun. 2015, 4(4), 90-94. doi: 10.11648/j.ajnc.20150404.12
AMA Style
Vahid Haji Hashemi, Abdorreza Alavi Gharahbagh. A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition. Am J Netw Commun. 2015;4(4):90-94. doi: 10.11648/j.ajnc.20150404.12
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Download@article{10.11648/j.ajnc.20150404.12,
author = {Vahid Haji Hashemi and Abdorreza Alavi Gharahbagh},
title = {A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition},
journal = {American Journal of Networks and Communications},
volume = {4},
number = {4},
pages = {90-94},
doi = {10.11648/j.ajnc.20150404.12},
url = {https://doi.org/10.11648/j.ajnc.20150404.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20150404.12},
abstract = {An efficient face recognition system using eigen values of wavelet transform as feature vectors and radial basis function (RBF) neural network as classifier is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet transformation.The wavelet coefficients obtained from the wavelet transformation are averaged for finding centers of features. In train process, four output of wavelet transform is analyzed and all eigenvalues of these images is obtained. At next step, the maximum 10 eigenvalues of wavelet sub images is stored as feature. Based on four sub images of wavelet transform and 10 eigenvalues of each sub image, the length of feature vector is 40. After obtaining features, in the train process for each person a center that has minimum Euclidean distance from all features is selected using RBF function. In fact the features are recognized by a RBF network. For a new input face image, firstly the feature vector is computed and then the distance (error) of this new vector with all centers of all persons is checked. The minimum distance is selected as target face. The proposed method on Essex face database and resultsshowed that the proposed method provide better recognition rates with low computational complexity.},
year = {2015}
}
TY - JOUR T1 - A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition AU - Vahid Haji Hashemi AU - Abdorreza Alavi Gharahbagh Y1 - 2015/07/08 PY - 2015 N1 - https://doi.org/10.11648/j.ajnc.20150404.12 DO - 10.11648/j.ajnc.20150404.12 T2 - American Journal of Networks and Communications JF - American Journal of Networks and Communications JO - American Journal of Networks and Communications SP - 90 EP - 94 PB - Science Publishing Group SN - 2326-8964 UR - https://doi.org/10.11648/j.ajnc.20150404.12 AB - An efficient face recognition system using eigen values of wavelet transform as feature vectors and radial basis function (RBF) neural network as classifier is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet transformation.The wavelet coefficients obtained from the wavelet transformation are averaged for finding centers of features. In train process, four output of wavelet transform is analyzed and all eigenvalues of these images is obtained. At next step, the maximum 10 eigenvalues of wavelet sub images is stored as feature. Based on four sub images of wavelet transform and 10 eigenvalues of each sub image, the length of feature vector is 40. After obtaining features, in the train process for each person a center that has minimum Euclidean distance from all features is selected using RBF function. In fact the features are recognized by a RBF network. For a new input face image, firstly the feature vector is computed and then the distance (error) of this new vector with all centers of all persons is checked. The minimum distance is selected as target face. The proposed method on Essex face database and resultsshowed that the proposed method provide better recognition rates with low computational complexity. VL - 4 IS - 4 ER -
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