Palmprint Recognition Using Different Level of Information Fusion

Authors

  • Siti Nur Wasilah Mohd Zuki School of Computer and Communication Engineering, Universiti Malaysia Perlis, KampusPauh Putra, 02600, Perlis
  • Muhammad Imran Ahmad School of Computer and Communication Engineering, Universiti Malaysia Perlis, KampusPauh Putra, 02600, Perlis
  • Ruzelita Ngadiran School of Computer and Communication Engineering, Universiti Malaysia Perlis, KampusPauh Putra, 02600, Perlis
  • Mohd Nazrin Md Isa School of Microelectronic Engineering, Universiti Malaysia Perlis, KampusPauh Putra, 02600, Perlis

Keywords:

Biometric System, Palmprint Recognition,

Abstract

The aim of this paper is to investigate a fusion approach suitable for palmprint recognition. Several number of fusion stageis analyse such as feature, matching and decision level. Fusion at feature level is able to increase discrimination power in the feature space by producing high dimensional fuse feature vector. Fusion at matching score level utilizes the matching output from different classifier to form a single value for decision process. Fusion at decision level on the other hand utilizes minimal information from a different matching process and the integration at this stage is less complex compare to other approach. The analysis shows integration at feature level produce the best recognition rates compare to the other method.

References

W. Zuo, D. Zhang, S. Member, K. Wang, and A. O. Problem, “Bidirectional PCA With Assembled Matrix Distance Metric for Image Recognition,” IEEE Transactions On Systems, Man, And

Cybernatics - Part B: Cybernatics,vol. 36, no. 4, pp. 863–872, 2006.

X. Pan and Q. Ruan, “Palmprint recognition using Gabor featurebased ( 2D ) 2 PCA,” Neurocomputing, vol. 71, pp. 3032–3036, 2008.

G. Lu, D. Zhang, and K. Wang, “Palmprint recognition using

eigenpalms features,” Pattern Recognition Letters,vol. 24, pp. 1463–

, 2003.

T. Connie, A. T. Jin, M. Goh, K. Ong, D. Ngo, and C. Ling, “An

automated palmprint recognition system,” Image and Vision

Computing, vol. 23, pp. 501–515, 2005.

T. Savi and N. Pave, “Personal recognition based on an image of the palmar surface of the hand,”Pattern Recognition, vol. 40, pp. 3152–3163, 2007.

Z. Zeng, “Palmprint Recognition using Gabor feature-based Twodirectional Two-dimensional Linear Discriminant Analysis,” IEEE International Conference on Electronic and Mechanical Engineering and Information Technology, pp. 1917–1921, 2011.

Y. Yao, X. Jing, and H. Wong, “Face and palmprint feature level fusion for single sample biometrics recognition,” Neurocomputing, vol. 70, pp. 1582–1586, 2007.

Y. Xu, D. Zhang, and J. Yang, “A feature extraction method for use with bimodal biometrics,” Pattern Recognition, vol. 43, no. 3, pp. 1106–1115, 2010.

J. Wang, W. Yau, A. Suwandy, and E. Sung, “Person recognition by fusing palmprint and palm vein images based on ‘ Laplacianpalm ’

representation,” Pattern Recognition, vol. 41, pp. 1514–1527, 2008.

X. Wu, D. Zhang, and K. Wang, “Fisherpalms based palmprint recognition,” Pattern Recognition Letters, vol. 24, pp. 2829–2838, 2003.

S. Arivazhagan, “Texture classification using Gabor wavelets based rotation invariant features,” Pattern Recognition Letters, vol. 27, pp. 1976–1982, 2006.

W. K. Kong, D. Zhang, and W. Li, “Palmprint feature extraction using 2-D Gabor filters,” Pattern Recognition, vol. 36, pp. 2339–2347, 2003.

A. Kong, D. Zhang, and M. Kamel, “Palmprint identification using

feature-level fusion,” Pattern Recognition, vol. 39, pp. 478–487,

J. Lee, “A novel biometric system based on palm vein image,” Pattern Recognition Letters, vol. 33, no. 12, pp. 1520–1528, 2012.

N. Saini and A. Sinha, “Face and palmprint multimodal biometric systems using Gabor – Wigner transform as feature extraction,” Pattern Analysis Application, vol. 18, no. 4, pp. 921–932, 2015.

J. You, W. Li, and D. Zhang, “Hierarchical palmprint identification

via multiple feature extraction,” Pattern Recognition, vol. 35, pp.

–859, 2002.

Y. Luo, L. Zhao, B. Zhang, W. Jia, F. Xue, J. Lu, Y. Zhu, and B.

Xu, “Local line directional pattern for palmprint recognition,”

Pattern Recognition, vol. 50, pp. 26–44, 2016.

W. Jia, B. Ling, K. Chau, and L. Heutte, “Palmprint identification

using restricted fusion,” Applied Mathematics and Computation,

vol. 205, no. 2, pp. 843–850, 2008.

W. Li, J. You, D. Zhang, and S. Member, “Texture-Based Palmprint Retrieval Using a Layered Search Scheme for

Personal Identification,” IEEE Transactions On Multimedia, vol. 7,

no. 5, pp. 891–898, 2005.

N. Duta, A. K. Jain, and K. V Mardia, “Matching of palmprints,” Pattern Recognition Letters, vol. 23, pp. 477–485, 2002.

M. Farmanbar and Ö. Toygar, “Feature selection for the fusion of face and palmprint biometrics,” Signal, Image Video Process., 2015.

A. Morales and M. A. F. A. Kumar, “Towards contactless palmprint authentication,” IET Computer Vision, vol. 5, Iss. 6, pp. 407–416, 2011.

D. Zhang, G. Lu, W. Li, S. Member, L. Zhang, and N. Luo, “Palmprint Recognition Using 3-D Information,” IEEE Transactions On Systems, Man, And Cybernatics - Part C: Applications and Reviews, vol. 39, no. 5, pp. 505–519, 2009.

G. Kah, O. Michael, T. Connie, and A. Teoh, “An innovative

contactless palm print and knuckle print recognition system,”

Pattern Recognition Letters, vol. 31, no. 12, pp. 1708–1719, 2010.

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,

Z. Le-qing and Z. San-yuan, “Multimodal biometric identification system based on finger geometry , knuckle print and palm print,” Pattern Recognition Letters, vol. 31, no. 12, pp. 1641–1649, 2010.

X. Pan, Q.-Q. Ruan, "Palmprint recognition with improved twodimensional locality preserving projections," Image and Vision

Computing, vol. 26, pp. 1261-1268, 2008.

S. Xu, J. Suo, J. Ding, "Improved linear Discriminant analysis based on two-dimensional Gabor for Palmprint recognition," IEEE

International Conference of Soft Computing and Pattern Recognition, pp. 157-160, 2011.

H. Imtiaz, S.A. Fattah, "A Wavelet-based dominant feature

extraction algorithm for palm-print recognition," Digital Signal

Processing, vol. 23, pp. 244-258, 2013.

C.-C. Han, H.-L.Cheng, C.-L. Lin, K.-C Fan, "Personal

authentication using palm-print features," Pattern Recognition, vol.

, pp. 371-381, 2003.

X.-Y. Jing, Y.-F.Yao, D. Zhang, J.-Y.Yang, M. Li, "Face and

palmprint pixel level fusion and Kernel DCV-RBF classifier for

small sample biometric recognition," Pattern Recognition, vol. 40,

pp. 3209-3224, 2007.

B. Zhang, W. Li, P. Qing, D. Zhang, "Palm-Print Classification by

Global Features," IEEE transactions On Systems, Man, And

Cybernatics: Systems, vol. 43, pp. 370-378, March 2013.

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Published

2016-07-01

How to Cite

Mohd Zuki, S. N. W., Ahmad, M. I., Ngadiran, R., & Md Isa, M. N. (2016). Palmprint Recognition Using Different Level of Information Fusion. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(4), 139–144. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1189