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A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition

Received: 15 June 2015     Accepted: 26 June 2015     Published: 8 July 2015
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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.

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

Keywords

Face Recognition, Singular Value Decomposition, SVD, Wavelet, Radial Basis Function, Neural Network

References
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[4] D. L. Swets and J. Weng, “Using Discriminant Eigen features for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, 1996, pp. 831-836.
[5] J. Yang, D. Zhang, A. F. Frangi and J.-Y. Yang, “Two- Dimensional PCA: A New Approach to Appearance- Based Face Representation and Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, 2004, pp. 131-138.
[6] A. N. Rajagopalan, K. S. Rao and Y. A. Kumar, “Face Recognition Using Multiple Facial Features,” Pattern Recognition Letters, Vol. 28, No. 3, 2007, pp. 335-341.
[7] B.-L. Zhang, H. H. Zhang and S. Z. S. Ge, “Face Recognition by Applying Wavelet Sub band Representation and Kernel Associative Memory,” IEEE Transactions on Neural Networks, Vol. 15, No. 1, 2005, pp. 166-177
[8] C. Garcia, G. Zikos and G. Tziritas, “Wavelet Packet Analysis for Face Recognition,” Image and Vision Computing, Vol. 18, No. 4, 2000, pp. 289-297.
[9] J. Z. Xue, H. Zhang and C. X. Zheng, “Wavelet Packet Transform for Feature Extraction of EEG during Mental Tasks,” Proceedings of the Second International Confer- ence on Machine Learning and Cybernetics, Vol. 1, 2003, pp. 360-363.
[10] O. Boumbarov, S. Sokolov and G. Gluhchev, “Combined Face Recognition Using Wavelet Packets and Radial Ba- sis Function Neural Network,” International Conference on Computer Systems and Technologies—CompSysTech’07, Bulgaria, 14-15 June 2007, pp. v.4.1-v.4.7.
[11] V. Perlibakas, “Face Recognition Using Principal Component Analysis and Wavelet Packet Decomposition,” Informatica, Vol. 15, No. 2, 2004, pp. 243-250.
[12] J.-T. Chien and C.-C. Wu, “Discriminant Wavelet faces and Nearest Feature Classifiers for Face Recognition,” IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. 24, No. 12, 2002, pp. 1644-1649.
[13] T. M. Mitchell, “Machine Learning,” China Machine Press, Beijing, 2003.
[14] H. Guo and J.-Y. Zhao, “Chinese Minority Script Recognition Using Radial Basis Function Network,” Journal of Computers, Vol. 5, No. 6, 2010, pp. 927-934.
[15] X.-Y. Jing, Y.-F. Yao, J.-Y. Yang and D. Zhang, “A Novel Face Recognition Approach Based on Kernel Dis- criminative Common Vectors (KDCV) Feature Extraction and RBF Neural Network,” Neuro computing, Vol. 71, No. 13-15, 2008, pp. 3044-3048.
[16] M. J. Er, S. Q. Wu, J. W. Lu and H. L. Toh, “Face Recognition with Radial Basis Function (RBF) Neural Net- works,” IEEE Transactions on Neural Networks, Vol. 13, No. 3, 2002, pp. 697-710.
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Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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    AU  - Vahid Haji Hashemi
    AU  - Abdorreza Alavi Gharahbagh
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    T2  - American Journal of Networks and Communications
    JF  - American Journal of Networks and Communications
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    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
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Author Information
  • Computer Engineering, Faculty of Engineering, Kharazmi University of Tehran,Tehran, Iran

  • Department of Electrical and Computer Engineering, Islamic Azad University, Shahrood, Iran

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