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Verification system robust to occlusion using low-order Zernike moments of palmprint sub-images

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Abstract

This paper proposes a palmprint based verification system which uses low-order Zernike moments of palmprint sub-images. Euclidean distance is used to match the Zernike moments of corresponding sub-images of query and enrolled palmprints. These matching scores of sub-images are fused using a weighted fusion strategy. The proposed system can also classify the sub-image of palmprint into non-occluded or occluded region and verify user with the help of non-occluded regions. So it is robust to occlusion. The palmprint is extracted from the acquired hand image using a low cost flat bed scanner. A palmprint extraction procedure which is robust to hand translation and rotation on the scanner has been proposed. The system is tested on IITK, PolyU and CASIA databases of size 549, 5239 and 7752 hand images respectively. It performs with accuracy of more than 98%, and FAR, FRR less than 2% for all the databases.

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References

  1. Arvacheh, E. M., & Tizhoosh, H. R. (2005). Pattern analysis using Zernike moments. In Instrumentation and measurement technology conference (pp. 1574–1578).

  2. Badrinath, G., & Gupta, P. (2007). An efficient multi-algorithm fusion system based on palmpring for personnel identification. In Intl. conf. on advanced computing (pp. 759–764).

  3. Bailey, R., & Srinath, M. (1996). Orthogonal moment features for use with parametric and nonparametric classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4), 389–399.

    Article  Google Scholar 

  4. Chen, J., Zhang, C., & Rong, G. (2001). Palmprint recognition using crease. In International conference on information processing (pp. 234–237).

  5. Choksuriwong, A., Laurent, H., & Emile, B. (2005). Comparison of invariant descriptors for object recognition. In IEEE international conference on image processing (pp. 377–380).

  6. Connie, T., Teoh, A., Ong, M., & Ngo, D. (2005). An automated palmprint recognition system. Image and Vision Computing, 23(5), 501–515.

    Article  Google Scholar 

  7. Gonzalez, R., Woods, R., & Eddins, S. (2004). Digital image processing using MATLAB(R). New York: Prentice Hall.

    Google Scholar 

  8. Haddadnia, J., Ahmadi, M., & Faez, K. (2003). An efficient method for recognition of human faces using higher orders pseudo Zernike moment invariant. International Journal of Pattern Recognition, 17(1), 41–62.

    Article  Google Scholar 

  9. Han, C., Cheng, H., Lin, C., & Fan, K. (2003). Personal authentication using palm-print features. Pattern Recognition, 36(2), 371–381.

    Article  Google Scholar 

  10. Hu, M. (1962). A visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8, 179–187.

    Google Scholar 

  11. Independent testing of iris recognition technology final report (2005). International Biometric Group.

  12. Jing, X.-Y., & Zhang, D. (2004). A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Transactions on Systems, Man, and Cybernetics B, 34(6), 2405–2415.

    Article  Google Scholar 

  13. Kumar, A., & Zhang, D. (2006). Personal recognition using hand shape and texture. IEEE Transactions on Image Processing, 15(8), 2454–2461.

    Article  Google Scholar 

  14. Mukundan, R., & Ramakrishnan, K. (1995). Computation of Legendre and Zernike moments. Pattern Recognition, 28(9), 1433–1442.

    Article  Google Scholar 

  15. Papakostas, G., Karras, D., Mertzios, B., & Boutalis, Y. (2005). An efficient feature extraction methodology for computer vision applications using wavelet compressed Zernike moments. ICGST International Journal on Graphics, Vision and Image Processing, SI1, 5–15.

    Google Scholar 

  16. Pavlidis, T. (1982). Algorithms for graphics and image processing. New York: Computer Science Press.

    Google Scholar 

  17. Ribaric, S., & Fratric, I. (2005). A biometric identification system based on eigenpalm and eigenfingerfeatures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1698–1709.

    Article  Google Scholar 

  18. Rowe, R., Uludag, U., Demirkus, M., Parthasaradhi, S., & Jain, A. (2007). A multispectral whole-hand biometric authentication system. In Biometrics Symposium (pp. 1–6).

  19. Shu, W., & Zhang, D. (1998). Automated personal identification by palmprint. Optical Engineering, 37(8), 2359–2362.

    Article  Google Scholar 

  20. Teague, M. (1980). Image analysis via the general theory of moments. Journal of the Optical Society of America (JOSA), 70(8), 920–930.

    Article  Google Scholar 

  21. Teh, C. H., & Chin, R. T. (1988). On image analysis by the methods of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4), 496–512.

    Article  Google Scholar 

  22. The CASIA palmprint database (2010). http://www.cbsr.ia.ac.cn/.

  23. The PolyU palmprint database (2010). http://www.comp.polyu.edu.hk/~biometrics.

  24. Wang, X., Gong, H., Zhang, H., Li, B., & Zhuang, Z. (2006). Palmprint identification using boosting local binary pattern. In Intl. conference on pattern recognition (pp. 503–506).

  25. Wenxin, L., Zhang, D., & Zhuoqun, X. (2002). Palmprint identification by Fourier transform. International Journal of Pattern Recognition and Artificial Intelligence, 16(4), 417–432.

    Article  Google Scholar 

  26. Wu, X., Zhang, D., & Wang, K. (2003). Fisherpalms based palmprint recognition. Pattern Recognition Letters, 24(15), 2829–2838.

    Article  Google Scholar 

  27. Zernike, F. (1934). Beugungstheorie des schneidenverfahrens und seiner verbesserten form, der phasenkontrastmethode. Physica, 1, 689–704.

    Article  Google Scholar 

  28. Zhang, D., & Shu, W. (1999). Two novel characteristics in palmprint verification:datum point invariance and line feature matching. Pattern Recognition, 32(4), 691–702.

    Article  Google Scholar 

  29. Zhang, D., Kong, A. W., You, J., & Wong, M. (2003). Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1041–1050.

    Article  Google Scholar 

  30. Zhang, L., & Zhang, D. (2004). Characterization of palmprints by wavelet signatures via directional context modeling. IEEE Transactions on Systems, Man, and Cybernetics, 34(3), 1335–1347.

    Article  Google Scholar 

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Correspondence to G. S. Badrinath.

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Badrinath, G.S., Kachhi, N.K. & Gupta, P. Verification system robust to occlusion using low-order Zernike moments of palmprint sub-images. Telecommun Syst 47, 275–290 (2011). https://doi.org/10.1007/s11235-010-9318-y

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