Fast and Efficient Palmprint Identification of a Small Sample within a Full Image

 

Carlos Francisco Moreno-García and Francesc Serratosa

 

¹ Universitat Rovira i Virgili, Departament d'Enginyeria Informàtica i Matemàtiques, Spain. carlosfrancisco.moreno@estudiants.urv.cat, francesc.serratosa@urv.cat

 

Article received on 04/09/2014.
Accepted on 03/11/2014.

 

Abstract

In some fields like forensic research, experts demand that a found sample of an individual can be matched with its full counterpart contained in a database. The found sample may present several characteristics that make this matching more difficult to perform, such as distortion and, most importantly, a very small size. Several solutions have been presented intending to solve this problem, however, big computational effort is required or low recognition rate is obtained. In this paper, we present a fast, simple, and efficient method to relate a small sample of a partial palmprint to a full one using elemental optimization processes and a voting mechanic. Experimentation shows that our method performs with a higher recognition rate than the state of the art method, when trying to identify palmprint samples with a radius as small as 2.64 cm.

Keywords: Sub-image registration, Hough method, candidate voting, Hungarian algorithm.

 

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References

1. Salvi, J., Matabosch, C., Fofi, D., & Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image Vision Comput., Vol. 25, No. 5, pp. 578-596.         [ Links ]

2. Ardeshir Goshtasby, A. (2005). 2-D and 3-D, Image Registration for Medical, Remote Sensing, and Industrial Applications. Wiley Press.         [ Links ]

3. Zitová, B. & Flusser, J. (2003). Image registration methods: a survey. Image Vision Comput., Vol. 21, No. 11, pp. 977-1000.         [ Links ]

4. Mikolajczyk, K. & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, No. 10, pp. 1615-1630.         [ Links ]

5. Zhang, Z. (1994). Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vision, Vol. 13, No. 2, pp. 119-152.         [ Links ]

6. Kuhn, H.W. (1955). The Hungarian method for the assignment problem Export. Naval Research Logistics Quarterly, Vol. 2, No. 1-2, pp. 83-97.         [ Links ]

7. Jonker, R. & Volgenant, T. (1986). Improving the Hungarian Assignement Algorithm. Operations Research Letters, Vol. 5, No. 4, pp. 171 -175.         [ Links ]

8. Fischler, M.A. & Bolles, R.C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, Vol. 24, No. 6, pp. 381 -395.         [ Links ]

9. Sanromà, G., Alquézar, R., Serratosa, F., & Herrera, B. (2012). Smooth Point-set Registration using Neighbouring Constraints. Pattern Recognition Letters, Vol. 33, No. 15, pp. 2029-2037.         [ Links ]

10. Kokiopoulou, E. & Frossard, P. (2010). Graphbased classification of multiple observation sets. Pattern Recognition, Vol. 43, No. 12, pp. 3988-3997.         [ Links ]

11. Wachinger, C. & Navab, N. (2013). Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization. IEEE Trans. on Pattern Analysis and Matching Intelligence, Vol. 35, No. 5, pp. 1221 -1233.         [ Links ]

12. Somvanshi, P. & Rane, M. (2012). Survey of Palmprint Recognition. International Journal of Scientific & Engineering Research, Vol. 3, No. 2.         [ Links ]

13. Zhang, D., Zuo, W., & Yue, F. (2012). A Comparative Study of Palmprint Recognition Algorithms. ACM Computing Surveys, Vol. 44, No. 1, p. 2.         [ Links ]

14. Funada, J., Ohta, N., Mizoguchi, M., Temma, T., Nakanishi, K., Murai, A., Sugiuchi, T., Wakabayashi, T., & Yamada, Y. (1998). Feature Extraction Method for Palmprint Considering Elimination of Creases. Proc. of 14th Int. Conf. Pattern Recognition, pp. 1849-1854.         [ Links ]

15. Kong, W.K. & Zhang, D. (2002). Using Low-Resolution Palmprint Images and Texture Analysis for Personal Identification. ICPR.         [ Links ]

16. Jain, A.K. & Demirkus, M. (2008). On Latent Palmprint Matching. MSU Technical Report.         [ Links ]

17. Jain, A.K. & Feng, J. (2009). Latent Palmprint Matching. IEEE Trans. on PAMI.

18. Dai, J. & Zhou, J. (2011). Multifeature-Based High-Resolution Palmprint Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 33, No. 5, pp. 945-957.         [ Links ]

19. Dai, J. & Zhou, J. (2012). Robust and Efficient Ridge Based Palmprint Matching. IEEE Transactions on Pattern Analysis and Matching Intelligence, Vol. 34, No. 8, pp. 1618-1632.         [ Links ]

20. Wang, X., Liang, J., & Wang, M. (2013). On-line fast palmprint identification based on adaptive lifting wavelet scheme. Knowledge-Based Systems, Vol. 42, pp. 68-73.         [ Links ]

21. Nibouche, O., Jiang, J. & Trundle, P. (2012). Analysis of performance of palmprint matching with enforced sparsity. Digital Signal Processing, Vol. 22, No. 2, pp. 348-355.         [ Links ]

22. Badrinath, G.S. & Gupta, P. (2012). Palmprint based recognition system using phase-difference information. Future Generation Computer Systems, Vol. 28, No. 1, pp. 287-305.         [ Links ]

23. Wang, X., Lei, L., & Wang, M. (2012). Palmprint verification based on 2D Gabor wavelet and pulse-coupled neural network. Knowledge-Based Systems, Vol. 27, pp. 451-455.         [ Links ]

24. Jain, A.K., Flynn, P., & Ross, A. (2009). Handbook of Biometrics. Springer.         [ Links ]

25. Ballard, D.H. (1980). Generalizing the Hough Transform to Detect Arbitrary Shapes. Ridge Based Palmprint Matching. IEEE Trans. on Pattern Analysis and Matching Intelligence.

26. Kassim, A.A., Tan, T., & Tan, K.H. (1999). A comparative study of efficient generalised Hough transform techniques. Image and Vision Computing, Vol. 17, pp. 737-748.         [ Links ]

27. Serratosa, F. (2014). Fast computation of bipartite graph matching. Pattern Recognition Letters, Vol. 45, pp. 244-250.         [ Links ]

28. Ratha, N.K., Karu, K., Chen, S., & Jain, A.K. (1996). A Real-Time Matching System for Large Fingerprint Databases. IEEE Trans. on PAMI, Vol. 18, No. 8, pp. 799-813.         [ Links ]