International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 1 - Issue 3, April 2012 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

A Novel Pre-processing Technique for DCT-domain Palm-print Recognition

[Full Text]



Hafiz Imtiaz, Shubhra Aich, Shaikh Anowarul Fattah



Spectral feature extraction, binary palm image, two-dimensional discrete cosine transform, classification, palm- print recognition, entropy, modularization.



In this paper, a novel pre-processing algorithm is introduced to identify the principal lines from a palm-print image and a discrete cosine transform (DCT) domain feature extraction algorithm is then employed for palm-print recognition, which can efficiently capture the spatial variations in the principal lines of a palm-print image. The entire image is segmented into several small spatial modules. The task of feature extraction is carried out in local zones using two dimensional discrete cosine transform (2D-DCT). The proposed dominant DCT-domain feature selection algorithm offers an advantage of very low feature dimension and it is capable of capturing precisely the detail variations within the palm-print image. It is shown that because of the pre-processing step, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.



[1] A. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4 – 20, 2004.

[2] A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,” Pattern Recognition, vol. 42, pp. 1408–1418, 2009.

[3] X. Wu, D. Zhang, and K. Wang, “Palm line extraction and matching for personal authentication,” IEEE Trans. Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 36, no. 5, pp. 978 –987, 2006.

[4] X. Wu, K. Wang, and D. Zhang, “Fuzzy direction element energy feature (FDEEF) based palmprint identification,” in Proc. Int. Conf. Pattern Recognition, vol. 1, 2002, pp. 95–98.

[5] S. Kung, S. Lin, and M. Fang, “A neural network approach to face/palm recognition,” in Proc. IEEE Workshop Neural Networks for Signal Pro- cessing, 1995, pp. 323–332.

[6] H. Imtiaz and S. A. Fattah, “A DCT-based feature extraction algorithm for palm-print recognition,” in Proc. IEEE Int. Conf. Communication Control and Computing Technologies (ICCCCT), 2010, pp. 657–660.

[7] ——, “A spectral domain dominant feature extraction algorithm for palm-print recognition,” International Journal of Image Processing, vol. 5, pp. 130–144, 2011.

[8] M. P. Dale, M. A. Joshi, and N. Gilda, “Texture based palmprint identification using DCT features,” in Proc. Int. Conf. Advances in Pattern Recognition, vol. 7, 2009, pp. 221–224.

[9] J. Lu, Y. Zhao, and J. Hu, “Enhanced gabor-based region covariance matrices for palmprint recognition,” Electron. Lett., vol. 45, pp. 880–881, 2009.