A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition
American Journal of Networks and Communications
Volume 4, Issue 4, August 2015, Pages: 90-94
Received: Jun. 15, 2015; Accepted: Jun. 26, 2015; Published: Jul. 8, 2015
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Authors
Vahid Haji Hashemi, Computer Engineering, Faculty of Engineering, Kharazmi University of Tehran,Tehran, Iran
Abdorreza Alavi Gharahbagh, Department of Electrical and Computer Engineering, Islamic Azad University, Shahrood, Iran
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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.
Keywords
Face Recognition, Singular Value Decomposition, SVD, Wavelet, Radial Basis Function, Neural Network
To cite this article
Vahid Haji Hashemi, Abdorreza Alavi Gharahbagh, A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition, American Journal of Networks and Communications. Vol. 4, No. 4, 2015, pp. 90-94. doi: 10.11648/j.ajnc.20150404.12
References
[1]
W. Zhao, R. Chellappa, A. Rosenfeld and P. J. Phillips, “Face Recognition: A Literature Survey,” Technical Re- port CAR-TR-948, University of Maryland, College Park, 2000.
[2]
M. Turk and A. Pentland, “Eigen faces for Recognition,” Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 71-86.
[3]
P. Belhumeur, J. Hespanha and D. Kriegman, “Eigen faces vs Fisher Faces: Recognition Using Class Specific Linear Projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 7, 1997, pp. 711-720.
[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.
[17]
B. C. Li and H. J. Yin, “Face Recognition Using RBF Neural Networks and Wavelet Transform,” Lecture Notes in Computer Science, Vol. 3497, 2005, pp. 105-111.
[18]
N. Jin and D. R. Liu, “Wavelet Basis Function Neural Networks for Sequential Learning,” IEEE Transactions on Neural Networks, Vol. 19, No. 3, 2008, pp. 523-528.
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