Abstract
Recognition of human faces is a very important task in many applications such as authentication and surveillance. An efficient face recognition system with face image representation using averaged wavelet and wavelet packet coefficients, Discriminative Common Vector (DCV) and modified Local Binary Patterns (LBP) and recognition using radial basis function (RBF) network is presented. Face images are decomposed by 2-level wavelet and wavelet packet transformation. The discriminative common vectors are obtained for averaged wavelet. The new proposed LBP operator is applied on the obtained DCV and also applied on averaged wavelet packet coefficients of all the samples of a class. The histogram values obtained from the LBP are recognized using RBF network. The proposed work is tested on three face databases such as Olivetti Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essex face database. The proposed method results in good recognition rates along with less training time because of the extracted discriminant input from the preprocessing steps involved in the proposed work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisher faces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis Machine Intelligence 20(7), 711–720 (1997)
Carlos, M.T., Marcos, D.P., Miguel, A.F., Jesus, B.A.: Reducing Features using Discriminative Common Vectors. Cognitive Computation 2, 160–164 (2010)
Cevikalp, H., Neamtu, M., Wilkes, M.: Discriminative common vectors method with kernels. IEEE Trans. Neural Network 17(6), 1550–1565 (2006)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Analysis Machine Intelligence 27(1), 4–13 (2005)
Er, M.J., Wu, S., Lu, J., Toh, H.L.: Face Recognition with Radial Basis Function (RBF) Neural Networks. IEEE Transactions on Neural Networks 13(3), 697–710 (2002)
Feng, G.C., Yuen, P.C., Dai, D.Q.: Human face recognition using PCA on wavelet subband. J. Electron. Imaging 9, 226–233 (2001)
Garcia, C., Zikos, G., Tziritas, G.: Wavelet packet analysis for face recognition. Image and Vision Computing 18, 289–297 (2000)
Jing, X.Y., Yao, Y.F., Yang, J.Y., Zhang, D.: A novel face recognition approach based on kernel discriminative common vectors (KDCV) feature extraction and RBF neural network. Neurocomputing 71, 3044–3048 (2008)
Kathirvalavakumar, T., Vasanthi, J.J.B.: Face representation using Wavelet, DCV and Modified Local Binary Patterns and Recognition by RBF. Journal of Machine Learning and Cybernetics (2013)
Li, B., Yin, H.: Face Recognition Using RBF Neural Networks and Wavelet Transform. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 105–111. Springer, Heidelberg (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Analysis Machine Intelligence 24(7), 971–987 (2002)
Pujol, A.F., Garca, J.C.: Computing the Principal Local Binary Patterns for face recognition using data mining tools. Expert Systems with Applications 39(8), 7165–7172 (2012)
Swets, D.L., Weng, J.: Using Discriminant Eigen features for Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(7), 71–86 (1991)
Perlibakas, V.: Face Recognition Using Principal Component Analysis and Wavelet Packet Decomposition. INFORMATICA 15(2), 243–250 (2004)
Wen, Y.: An improved discriminative common vectors and support vector machine based face recognition approach. Expert Systems with Applications 39(4), 4628–4632 (2012)
Wong, Y.W., Seng, K.P., Ang, L.M.: Dual optimal multiband features for face recognition. Expert Systems with Applications 37(4), 2957–2962 (2010)
Wong, Y.W.: Radial Basis Function Neural Network with Incremental Learning for Face Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(4), 940–949 (2011)
Zhang, B.L., Zhang, H., Ge, S.S.: Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory. IEEE Transactions on Neural networks 15(1), 166–177 (2005)
Zhou, S.R., Yin, J.P., Zhang, J.M.: Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation. Neurocomputing 116(20), 260–264 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Kathirvalavakumar, T., Vasanthi, J.J.B. (2013). Face Representation Using Averaged Wavelet, Micro Patterns and Recognition Using RBF Network. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-03844-5_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
eBook Packages: Computer ScienceComputer Science (R0)