An Improved Principal Component Analysis for Palmprint Recognition


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Abstract


In this paper, an improved principal component analysis method of is proposed. In this approach, firstly, palmprint features are extracted by the Gabor wavelets. Then, the bi-directional compressed 2DPCA is applied to remove redundant information from the image rows and columns. Finally, principal component analysis is used to further reduce the dimension. The proposed method has greater palmprint recognition accuracy at the same time reducing the dimension. Experiment results show that the effectiveness of the proposed method is verified using the PolyU palmprint databases.
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Keywords


2DPCA; Gabor Wavelets; Palmprint Feature; Principal Component Analysis

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References


G. Lu, D. Zhang and K.Wang, Palmprint recognition using eigenpalms features, Pattern Recognit., vol.24, no.10, pp. 1463–1467, 2003.

X. Wu, D. Zhang and K. Wang, Fisherpalms based palmprint recognition, Pattern Recognit, vol.24, no.15, pp. 2829–2838,2003.

X. Wu, D. Zhang, K. Wang, and B. Huang, Palmprint classification using principal lines, Pattern Recognit., vol.37, no.10, pp. 1987–1998,2004.

A. Poinsot, F. Yang. Palmprint and Face Score Level Fusion for Contactless Small Sample Biometric Recognition and Verification., International Review on Computers and Software, Vol. 5, n. 2, pp. 156-167, 2010.

C. J. Liu and H. Wechsler, Independent Component Analysis of Gabor Features for Face Recognition, IEEE Transaction on Neural Networks, vol.14, no. 4, pp. 658–669, 2003.

C. Liu, Gabor-based kernel PCA with fractional power polynomial models for face recognition, IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 5, pp. 572–581,2004.

Y. Pang, Y. Yuan and X. li, Gabor-base region covariance matrices for face recognition, IEEE Trans. Circuit Syst. Video Technol., vol. 18, no. 7, pp.989–993, 2008.

Nooshin Jafari Fesharaki, Hossein Pourghassem. Medical X-ray Image Clustering Using a New Gabor Function-Based Image Representation, International Review on Computers and Software, Vol. 7,n. 3, pp. 1100-1106, 2012.

X. Yang, Y. Zhou, T. Zhang, and J. Yang, “Gabor phase based gait recognition,” Eletron. Lett., vol. 44, no. 10, pp. 620–621, 2008.

J. Yang, D. Zhang, A. F. Frangi and J. Y. Yang, “Two dimensional PCA: A new approach to appereance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell, vol.26, no.1, pp. 131–137, 2004.

T. Hong, H. Kim and H. Moon, Face Recognition using the Gabor Wavelet and 2-Dimensional PCA, International Journal of Computer Science and Network Security, vol. 5, no. 11, pp. 915–919, 2005.

D. J. Field, Relation between the statistics of natural images and the response properties of cortical cells, J. Opt. Soc. Am., pp. 2379–2394, 1987.


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