Abstract

In order to effectively carry out the fingerprint image recognition, this study proposes a fingerprint preprocessing algorithm and feature extraction technology. In the fingerprint image preprocessing, this study gives the location method of region of interest. In terms of feature extraction, this study extracts the decomposition energy features of fingerprint. In terms of matching recognition, this study presents a new recognition method for fingerprint and uses the method to carry out the matching and recognition of fingerprint. In the simulation, the presented fingerprint recognition method is compared with several existing major fingerprinting methods. The comparison results show that the proposed recognition method is more accurate and faster than some existing better recognition methods. Its recognition precision is higher, the recognition speed is faster and the anti-noise ability is stronger. These researches in this study on fingerprinting promote an improving of the accuracy and speed of recognition and provide a new way of thinking for target recognition, which has an important theoretical references and practical significance.

Keywords:

Feature extraction, fingerprint recognition, image preprocessing, wavelet packet transforms,

References

1. Aditya, A. and S. Stephanie, 2009. Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recogn., 42(3): 452-464.
   CrossRef    
2. Caldwell, T., 2013. Tabletop combines image display and fingerprint recognition. Biometric Technol. Today, 8: 9-12.
   CrossRef    
3. Edward, H.S.L., R.P. Mark, R.F. Michael and F.A. John, 2011. Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform. Image Vision Comput., 29(1): 15-28.
   CrossRef    
4. Fan, D., P. Yu, P. Du, W. Li and X. Cao, 2012. A novel probabilistic model based fingerprint recognition algorithm. Proc. Eng., 29: 201-206.
   CrossRef    
5. Inyang, U.G. and O.C. Akinyokun, 2014. A hybrid knowledge discovery system for oil spillage risks pattern classification. J. Artif. Intell. Res., 3(4): 77-86.
   CrossRef    
6. Jiang, X., X. You, Y. Yuan and M. Gong, 2012. A method using long digital straight segments for fingerprint recognition. Neurocomputing, 77(1): 28-35.
   CrossRef    
7. Kizrak Ayyüce, M. and F. Özen, 2011. A new median filter based fingerprint recognition algorithm. Proc. Comput. Sci., 3: 859-865.
   CrossRef    
8. Lin, C.H., J.L. Chen and C.Y. Tseng, 2011. Optical sensor measurement and biometric-based fractal pattern classifier for fingerprint recognition. Expert Syst. Appl., 38(5): 5081-5089.
   CrossRef    
9. Lumini, A. and L. Nanni, 2008. Advanced methods for two-class pattern recognition problem formulation for minutiae-based fingerprint verification. Pattern Recogn. Lett., 29(2): 142-148.
   CrossRef    
10. Moros, J., J. Serrano, F.J. Gallego, J. Macías and J.J. Laserna, 2013. Recognition of explosives fingerprints on objects for courier services using machine learning methods and laser-induced breakdown spectroscopy. Talanta, 110: 108-117.
    CrossRef    PMid:23618183    
11. Nanni, L. and A. Lumini, 2006. A novel method for fingerprint verification that approaches the problem as a two-class pattern recognition problem. Neurocomputing, 69(7-9): 846-849.
    CrossRef    
12. Nikou, C., 2007. A class-adaptive spatially variant mixture model for image segmentation. IEEE T. Image Proc., 16(4): 1121-1130.
    CrossRef    
13. Vatsa, M., R. Singh, A. Noore and K. Morris, 2011. Simultaneous latent fingerprint recognition. Appl. Soft Comput., 11(7): 4260-4266.
    CrossRef    
14. Wang, L., X. Wang and L. Kong, 2012. Automatic authentication and distinction of Epimedium koreanum and Epimedium wushanense with HPLC fingerprint analysis assisted by pattern recognition techniques. Biochem. Syst. Ecol., 40: 138-145.
    CrossRef    
15. Xie, X., J. Wu and M. Jing, 2013. Fast two-stage segmentation via non-local active contours in multiscale texture feature space. Pattern Recogn. Lett., 34(11): 1230-1239.
    CrossRef    
16. Yu, J., 2011. Texture segmentation based on FCM algorithm combined with GLCM and space information. Proceeding of the International Conference on Electric Information and Control Engineering, pp: 4569 -4572.
    PMid:21844240 PMCid:PMC3257907    
17. Zhang, J., X.J. Jing, N. Chen and J.L. Wang, 2013. Incomplete fingerprint recognition based on feature fusion and pattern entropy. J. China Univ. Posts Telecommun., 20(3): 121-128.
    CrossRef    
18. Zhou, H., J. Zheng and L. Wei, 2013. Texture aware image segmentation using graph cuts and active contours. Pattern Recogn., 46(6): 1719-1733.
    CrossRef    

Competing interests

The authors have no competing interests.

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The authors have no competing interests.