Abstract
In this paper, a feature extraction method is proposed by combining Wavelet Transformation (WT) and Non-negative Matrix Factorization with Sparseness constraints (NMFs) together for normal face images and partially occluded ones. Firstly, we apply two-level wavelet transformation to the face images. Then, the low frequency sub-bands are decomposed according to NMFs to extract either the holistic representations or the parts-based ones by constraining the sparseness of the basis images. This method can not only overcome the the low speed and recognition rate problems of traditional methods such as PCA and ICA, but also control the sparseness of the decomposed matrices freely and discover stable, intuitionistic local characteristic more easily compared with classical non-negative matrix factorization algorithm (NMF) and local non-negative matrix decomposition algorithm (LNMF). The experiment result shows that this feature extraction method is easy and feasible with lower complexity. It is also insensitive to the expression and the partial occlusion, obtaining higher recognition rate. Moreover, the WT+NMFs algorithm is robust than traditional ones when the occlusion is serious.
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.
References
Lee, D.D., Seung, H.S.: Unsupervised Learning by Convex and Conic Coding. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, pp. 515–521. The MIT Press, Massachusetts (1997)
Feng, T., Stan, Z., Li, H.Y.S., Zhang, H.J.: Local Non-negative Matrix Factorization as a Visual Representation. In: 2nd International Conference on Development and Learning, Cambridge, pp. 7695–1459 (2002)
Patrik, O.H.: Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)
Manthalkar, R., Biswas, P.K., Chatterji, B.N.: Rotation and Scale Invariant Texture Features Using Discrete Wavelet Packet Transform. Pattern Recognition Letter 24, 2452–2462 (2003)
Lee, D., Seung, H.S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 1401, 788–791 (1999)
Pu, X.R., Zhang, Y., Zheng, Z.M., Wei, Z., Mao, Y.: Face Recognition Using Fisher Non-negative Matrix Factorization with Sparseness Constraints. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 112–117. Springer, Heidelberg (2005)
Ouyang, Y.B., Pu, X.R., Zhang, Y.: Wavelet-based Non-negative Matrix Factorization with Sparseness Constraints for Face Recognition. Application Research of Computers 10, 159–162 (2006)
Chen, W.G., Qi, F.H.: Learning NMF Representation Using a Hybrid Method Combining Feasible Direction Algorithm and Simulated Annealing. Acta Electronica Sinica 31, 2190–2193 (2003)
Zhang, Z.W., Yang, F., Xia, K.W., Yang, R.X.: Research on Face Recognition Method Based on Wavelet Transform and NMF. Computer Engineering 33, 176–179 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, S., Deng, W., Miao, D. (2008). A Feature Extraction Method Based on Wavelet Transform and NMFs. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)