Abstract
In this paper, a novel and efficient method for recognizing palmprint based on radial basis probabilistic neural networks committee (RBPNNC) was proposed. The RBPNNC consists of several different independent neural networks trained by different feature domains of the original images. The final classification results represent a combined response of the individual networks. The Hong Kong Polytechnic University (PolyU) palmprint database is exploited to test our approach. The experimental results show that the RBPNNC achieves higher recognition accuracy and better classification efficiency than single feature domain.
This work was supported by the Postdoctoral Science Foundation of China (NO.20060390180), Scientific Research Foundation of Huaqiao University (NO.06BS217), and the Youth Technological Talent Innovative Project of Fujian Province (NO.2006F3086).
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References
Connie, T., Teoh, A., Goh, M., et al.: Palmprint Recognition with PCA and ICA. In: Image and Vision Computing New Zealand 2003, Palmerston North, New Zealand, pp. 227–232 (2003)
Kumar, A., Shen, H.C.: Recognition of Palmprints using Wavelet-based Features. In: Proceedings of the International Conference on Systems and Cybernetics, SCI-2002, Orlando, Florida, July 2002, pp. 371–376 (2002)
Li, W., David, Z., Xu, Z.: Palmprint Identification by Fourier transform. Int. J. Pattern Recognition Art. Intell. 16, 417–432 (2002)
Wu, X., Zhang, D., Wang, K.: Fisherpalms based on Palmprint Recognition. Pattern Recognition Letter 24, 2829–2938 (2003)
Shang, L., Huang, D.-S., Du, J.-X., Huang, Z.-K.: Palmprint Recognition Using ICA Based on Winner-Take-All Network and Radial Basis Probabilistic Neural Network. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 216–221. Springer, Heidelberg (2006)
Shang, L., Huang, D.S., Du, J.X., Zheng, C.H.: Palmprint Recognition using FastICA Algorithm and Radial Basis Probabilistic Neural Network. Neurocomputing 69, 1782–1786 (2006)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13, 1083–1101 (1999)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)
Su, M., Basu, M.: Gating Improves Neural Network Performance. In: Proceedings IJCNN’01, vol. 3, pp. 2159–2164 (2001)
Hanaen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Large Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)
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Du, J., Zhai, C., Wan, Y. (2007). Radial Basis Probabilistic Neural Networks Committee for Palmprint Recognition. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_98
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DOI: https://doi.org/10.1007/978-3-540-72393-6_98
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