Abstract
This paper proposed a novel and successful method for recognizing palmprint using 2D-Gabor wavelet filter based sparse coding (SC) algorithm and the radial basis probabilistic neural network (RBPNN) classifier proposed by us. Features of Palmprint images are extracted by this SC algorithm, which exploits feature coefficients’ Kurtosis as the maximum sparse measure criterion and a variance term of sparse coefficients as the fixed information capacity. At the same time, in order to reduce the iteration time, features of 2D-Gabor wavelet filter are also used as the initialization feature matrix. The RBPNN classifier is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS algorithm (ROLSA). Experimental results show that this SC algorithm is successful in extracting features of palmprint images, and the RBPNN model achieves higher recognition rate and better classification efficiency with other usual classifiers.
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
Julian, E., Körner, E.: Sparse coding and NMF. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2529–2533 (2004)
Hoyer, P.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1427–1469 (2004)
Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 401, 788–791 (1999)
Olshausen, B.A., Field, D.J.: Emergence of Simple-cell Receptive Field Properties by Learning a Sparse Code for Natural Images. Nature 381, 607–609 (1996)
Shang, L., Cao, F., Chen, J.: Denoising Natural Images Using Sparse Coding Algorithm Based on the Kurtosis Measurement. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W., et al. (eds.) Euro-Par 2008. LNCS, vol. 5264, pp. 351–358. Springer, Heidelberg (2008)
Bell, A., Sejnowski, T.J.: The ‘Independent Components’ of Natural Scenes Are Edge Filters. Vision Research 37, 3327–3338 (1997)
Hyvärinen, A., Hoyer, P.O.: Independent Component Analysis Applied to Feature Extraction from Colour and Stereo Images. Network Computation in Neural Systems 11(3), 191–210 (2000)
Hyvärinen, A.: Sparse Coding Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation. Neural Computation 11, 1739–1768 (1997)
Shang, L., Zhang, J., Huai, W., et al.: Image Reconstruction Using NMF with Sparse Constraints Based on Kurtosis Measurement Criterion. In: Huang, D.S., Jo, K.-H., Lee, H.-H., et al. (eds.) Euro-Par 1996. LNCS, vol. 5755, pp. 834–840. Springer, Heidelberg (1996)
Hyvärinen, A., Oja, E., Hoyer, P., Horri, J.: Image Feature Extraction by Sparse Coding and Independent Component Analysis. In: 19th Proc. Int. Conf. on Pattern Recognition (ICPR 1998), pp. 1268–1273. IEEE Press, New Work (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shang, L., Huai, W., Dai, G., Chen, J., Du, J. (2010). Palmprint Recognition Using 2D-Gabor Wavelet Based Sparse Coding and RBPNN Classifier. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)