Palmprint Recognition Using Gabor-Based Scale Orientation



Various methods are used to obtain a superior palmprint recognition system. After selecting the palmprint image filter, using the Gabor orientation scale pair becomes an option to support the improvement of the verification process. The $ [8\times 7] $ pair of the Gabor orientation scale pair provides a significant system improvement impact from several alternatives. Although many researchers in the same field use different options by getting as many as 40 different positions, with differences as many as 56 parts, Gabor does not take up computational time. The system will be more superior when it combines the use of ThreeW filter, KPCA dimension reduction, and cosine matching method to get a verification rate of $ 99,611\% $. With the achievement of the results of this study, it can be an alternative system in the field of palmprint recognition.

Author Biography

Muhammad Kusban, Electrical UMS

Electrical Engineering


K. Bensid, D. Samai, F. Z. Laallam, and A. Meraoumia, “Deep learning

feature extraction for multispectral palmprint identification,” Journal

of Electronic Imaging, vol. 27, no. 3, pp. 1 – 11, 2018. [Online].


N. Saini and A. Sinha, “Efficient fusion of face and palmprint in

gabor filtered wigner domain,” International Journal of Biometrics,

vol. 12, no. 3, pp. 301–316, 2020. [Online]. Available: https:


Y. Aberni, L. Boubchir, and B. Daachi, “Multispectral palmprint recognition:

A state-of-the-art review,” 07 2017, pp. 793–797.

C. L. Deepika, A. Kandaswamy, C. Vimal, and B. Satish, “Palmprint

authentication using modified legendre moments,” Procedia Computer

Science, vol. 2, pp. 164 – 172, 2010. [Online]. Available: http:


J. Sung, S.-Y. Bang, and S. Choi, “A bayesian network classifier

and hierarchical gabor features for handwritten numeral recognition,”

Pattern Recognition Letters, vol. 27, no. 1, pp. 66 – 75, 2006.

[Online]. Available:


H. jun Wang, H. nian Qi, and X. F. Wang, “A new Gabor based approach

for wood recognition,” Neurocomputing, vol. 116, pp. 192–200, 2013.

[Online]. Available:

Q. Li, X. Li, Z. Guo, and J. You, “Online personal verification by

palmvein image through palmprint-like and palmvein information,”

Neurocomputing, vol. 147, no. Supplement C, pp. 364 – 371,

, advances in Self-Organizing Maps Subtitle of the special

issue: Selected Papers from the Workshop on Self-Organizing Maps

(WSOM 2012). [Online]. Available: http://www.sciencedirect.


G. S. Badrinath and P. Gupta, “Palmprint based recognition system

using phase-difference information,” Future Generation Computer

Systems, vol. 28, no. 1, pp. 287–305, 2012. [Online]. Available:

M. Aykut and M. Ekinci, Kernel Principal Component Analysis

of Gabor Features for Palmprint Recognition. Berlin, Heidelberg:

Springer Berlin Heidelberg, 2009, pp. 685–694. [Online]. Available: 70

Y. Xu, L. Fei, and D. Zhang, “Combining left and right palmprint images

for more accurate personal identification,” IEEE Transactions on Image

Processing, vol. 24, no. 2, pp. 549–559, Feb 2015.

C. A. Perez, L. A. Cament, and L. E. Castillo, “Methodological

improvement on local gabor face recognition based on feature selection

and enhanced borda count,” Pattern Recognition, vol. 44, no. 4, pp.

– 963, 2011. [Online]. Available: //


Y. Xu, D. Zhang, and J.-Y. Yang, “A feature extraction method for

use with bimodal biometrics,” Pattern Recognition, vol. 43, no. 3,

pp. 1106–1115, 2010. [Online]. Available:


B. Zhang, W. Li, P. Qing, and D. Zhang, “Palm-print classification by

global features,” IEEE Transactions on Systems, Man, and Cybernetics:

Systems, vol. 43, no. 2, pp. 370–378, March 2013. [Online]. Available:

G. K. Ong Michael, T. Connie, and A. B. Jin Teoh, “A Contactless

Biometric System Using Palm Print and Palm Vein Features,”

in Advanced Biometric Technologies. InTech, aug 2011. [Online].


I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, “Euclidean

distance matrices: Essential theory, algorithms, and applications,” IEEESignal Processing Magazine, vol. 32, no. 6, pp. 12–30, Nov 2015.

M. Velasquez and P. Hester, “An analysis of multi-criteria decision

making methods,” International Journal of Operations Research, vol. 10,

pp. 56–66, 05 2013.

A. Kumar and D. Zhang, “Palmprint authentication using multiple

classifiers,” in Proceedings of SPIE - The International Society for

Optical Engineering, vol. 5404, 2004, pp. 20–29.

V. ˇ Struc and N. P. C, “Gabor-Based Kernel Partial-Least-Squares

Discrimination Features for Face Recognition,” Informatica, vol. 20,

no. 1, pp. 115–138, 2009. [Online]. Available:

H. jun Wang, H. nian Qi, and X.-F. Wang, “A new gabor based

approach for wood recognition,” Neurocomputing, vol. 116, pp. 192 –

, 2013. [Online]. Available:


R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features

for image classification,” IEEE Transactions on Systems, Man, and

Cybernetics, vol. SMC-3, no. 6, pp. 610–621, Nov 1973. [Online].







Image Processing