Effective statistical-based and dynamic fingerprint preprocessing technique

Effective statistical-based and dynamic fingerprint preprocessing technique

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Challenges to contextual filtering techniques include difficulties in estimating orientation field in poor quality images and subsequently failure to extract ridges reliably in large regions of low quality. This study proposes a statistical-based and dynamic fingerprint preprocessing technique for adaptive contrast enhancement and binarisation of fair and poor qualities plain and rolled fingerprints with large regions of low quality, prior to orientation field estimation. The algorithm effectively enhances smudged and faded ridges uniformly in recoverable regions, based on values of statistical variables computed locally in each region. The preprocessing algorithm employs a locally adaptive thresholding approach resulting in enhanced binarised images. The performance of the proposed algorithm was determined by carrying out biometric verification evaluation using a popular commercial biometric matching software, on databases of fingerprints in their original forms, as well as same fingerprints enhanced with the proposed algorithm. Experiments show that fingerprints are uniformly enhanced and binarised; and smudged or faded ridges in recoverable regions made visible. Fingerprint verification evaluation on preprocessed fingerprints resulted in lower error rates in 12 databases. These results show that the proposed algorithm significantly improves recognition.


    1. 1)
      • 1. Jing-Wein, W., Ngoc Tuyen, L., Chou-Chen, W., et al: ‘Enhanced ridge structure for improving fingerprint image quality based on a wavelet domain’, IEEE Signal Process. Lett., 2015, 22, pp. 390394.
    2. 2)
      • 2. Jian-Liung, C., Cong-Hui, H., Yi-Chun, D., et al: ‘Combining fractional-order edge detection and chaos synchronisation classifier for fingerprint identification’, IET Image Process., 2014, 8, pp. 354362.
    3. 3)
      • 3. Short, N.J., Abbott, A.L., Hsiao, M.S., et al: ‘Reducing descriptor measurement error through bayesian estimation of fingerprint minutia location and direction’, IET Biometrics, 2012, 1, pp. 8290.
    4. 4)
      • 4. Hong, L., Wan, Y., Jain, A.: ‘Fingerprint image enhancement: algorithm and performance evaluation’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, pp. 777789.
    5. 5)
      • 5. Ratha, N.K., Chen, S., Jain, A.K.: ‘Adaptive flow orientation-based feature extraction in fingerprint images’, Pattern Recogn., 1995, 28, pp. 16571672.
    6. 6)
      • 6. Kai, C., Jain, A.K.: ‘Learning fingerprint reconstruction: from minutiae to image’, IEEE Trans. Inf. Forensics Sec., 2015, 10, pp. 104117.
    7. 7)
      • 7. Leung, K.C., Leung, C.H.: ‘Fingerprint retrieval by spatial modelling and distorted sample generation’, IET Comput. Vis., 2013, 7, pp. 425436.
    8. 8)
      • 8. Chang, S., Cheng, Y., Larin, K.V., et al: ‘Optical coherence tomography used for security and fingerprint-sensing applications’, IET Image Process, 2008, 2, pp. 4858.
    9. 9)
      • 9. Chen, F., Huang, X., Zhou, J.: ‘Hierarchical minutiae matching for fingerprint and palmprint identification’, IEEE Trans. Image Process., 2013, 22, pp. 49644971.
    10. 10)
      • 10. Paulino, A.A., Feng, J., Jain, A.K.: ‘Latent fingerprint matching using descriptor-based hough transform’, IEEE Trans. Inf. Forensics Sec., 2013, 8, pp. 3145.
    11. 11)
      • 11. Nandakumar, K.: ‘Fingerprint matching based on minutiae phase spectrum’. 2012 5th IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 216221.
    12. 12)
      • 12. Liu, S., Liu, M.: ‘Fingerprint orientation modeling by sparse coding’. 2012 5th IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 176181.
    13. 13)
      • 13. Gupta, P.: ‘Fingerprint orientation modeling using symmetric filters’. 2015 IEEE Winter Conf. on Applications of Computer Vision, 2015, pp. 663669.
    14. 14)
      • 14. Surya, A.A.K., Nugroho, A.S., Lim, C.: ‘Evaluation of fingerprint orientation field correction methods’. Int. Conf. on Advanced Computer Science and Information System (ICACSIS), 2011, pp. 353358.
    15. 15)
      • 15. Tao, X., Yang, X., Cao, K., et al: ‘Estimation of fingerprint orientation field by weighted 2D Fourier expansion model’. 20th Int. Conf. on Pattern Recognition (ICPR), 2010, pp. 12531256.
    16. 16)
      • 16. Gottschlich, C., Mihailescu, P., Munk, A.: ‘Robust orientation field estimation and extrapolation using semilocal line sensors’, IEEE Trans. Inf. Forensics Sec., 2009, 4, pp. 802811.
    17. 17)
      • 17. Chen, X.: ‘PDE-based regularization of orientation field for low-quality fingerprint images’. IEEE 11th Int. Conf. on Signal Processing (ICSP), 2012, pp. 10061011.
    18. 18)
      • 18. Sutthiwichaiporn, P., Areekul, V.: ‘Adaptive boosted spectral filtering for progressive fingerprint enhancement’, Pattern Recogn., 2013, 46, pp. 24652486.
    19. 19)
      • 19. Liang, X., Asano, T.: ‘A linear time algorithm for binary fingerprint image denoising using distance transform’, IEICE Trans. Inf. Syst., 2006, E89-D, pp. 110.
    20. 20)
      • 20. Yuheng, Z., Qinghan, X.: ‘An optimized approach for fingerprint binarization’. Int. Joint Conf. on Neural Networks. IJCNN ’06, 2006, pp. 391395.
    21. 21)
      • 21. Munshi, P., Mitra, S.K.: ‘A rough-set based binarization technique for fingerprint images’. 2012 IEEE Int. Conf. on Signal Processing, Computing and Control (ISPCC), 2012, pp. 16.
    22. 22)
      • 22. Guo, L., Chen, D.-H., Li, H., et al: ‘Characteristic preserving binarization for fingerprint image’. Fourth Int. Conf. on Image and Graphics, ICIG, 2007, pp. 401408.
    23. 23)
      • 23. Bartunek, J.S., Nilsson, M., Nordberg, J., et al: ‘Adaptive fingerprint binarization by frequency domain analysis’. Fortieth Asilomar Conf. on Signals, Systems and Computers. ACSSC ’06, 2006, pp. 598602.
    24. 24)
      • 24. Bartunek, J.S., Nilsson, M., Nordberg, J., et al: ‘Improved adaptive fingerprint binarization’. Congress on Image and Signal Processing, CISP ’08, 2008, pp. 756760.
    25. 25)
      • 25. Jing-Jing, G., Mei, X.: ‘The layered segmentation, gabor filtering and binarization based on orientation for fingerprint preprocessing’. 8th Int. Conf. on Signal Processing, 2006.
    26. 26)
      • 26. Hailong, J., Kun, C.: ‘The research on the preprocessing algorithm for fingerprint image’. 2012 IEEE Symp. on Electrical and Electronics Engineering (EEESYM), 2012, pp. 163166.
    27. 27)
      • 27. Kheiri, F., Samavi, S., Karimi, N.: ‘A new pipeline design for binarization and thinning of fingerprint images’. Canadian Conf. on Electrical and Computer Engineering, 2005, pp. 20132016.
    28. 28)
      • 28. Medeiros, L.X., Flores, E.L., Arantes Carrijo, G., et al: ‘Optimization of calculation of field orientation time and binarization of fingerprint images’, IEEE Latin Am. Trans. (Revista IEEE America Latina), 2011, 9, pp. 868874.
    29. 29)
      • 29. Jun, L., Wu, H., Kangling, F., et al: ‘A wavelet-transform-based binarization algorithm on dynamic threshold of vertical orientation of fingerprint’. Proc. of the 2004 Int. Conf. on Intelligent Mechatronics and Automation, 2004, pp. 877880.
    30. 30)
      • 30. Cheng, J., Tian, J.: ‘Fingerprint enhancement with dyadic scale-space’, Pattern Recogn. Lett., 2004, 25, pp. 12731284.
    31. 31)
      • 31. Ghafoor, M., Taj, I.A., Ahmad, W., et al: ‘Efficient 2-fold contextual filtering approach for fingerprint enhancement’, IET Image Process., 2014, 8, pp. 417425.
    32. 32)
      • 32. Wang, W., Li, J., Huang, F., et al: ‘Design and implementation of Log-Gabor filter in fingerprint image enhancement’, Pattern Recogn. Lett., 2008, 29, pp. 301308.
    33. 33)
      • 33. Gottschlich, C.: ‘Curved-region-based ridge frequency estimation and curved gabor filters for fingerprint image enhancement’, IEEE Trans. Image Process., 2012, 21, pp. 22202227.
    34. 34)
      • 34. Gottschlich, C., Schoenlieb, C.B.: ‘Oriented diffusion filtering for enhancing low-quality fingerprint images’, IET Biometrics, 2012, 1, pp. 105113.
    35. 35)
      • 35. Jucheng, Y., Naixue, X., Vasilakos, A.V.: ‘Two-stage enhancement scheme for low-quality fingerprint images by learning from the images’, IEEE Trans. Human-Mach. Syst., 2013, 43, pp. 235248.
    36. 36)
      • 36. Sherlock, B.G., Monro, D.M., Millard, K.: ‘Fingerprint enhancement by directional Fourier filtering’, IEE Proc. – Vis. Image Signal Process., 1994, 141, pp. 8794.
    37. 37)
      • 37. Coetzee, L., Botha, E.C.: ‘Fingerprint recognition in low quality images’, Pattern Recogn., 1993, 26, pp. 14411460.
    38. 38)
      • 38. Griaule_Biometrics. Grfinger X 4.2. Available at
    39. 39)
      • 39. Neurotechnology. VeriFinger SDK 8.0. Available at
    40. 40)
      • 40. Jain, A.K., Cao, K.: ‘Fingerprint image analysis: role of orientation patch and ridge structure dictionaries’, in ‘Geometry driven statistics’ (John Wiley & Sons, Ltd, 2015), pp. 288310.
    41. 41)
      • 41. Maio, D., Maltoni, D., Cappelli, R., et al: ‘FVC2000: fingerprint verification competition’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, pp. 402412.
    42. 42)
      • 42. Maltoni, D., Maio, D., Jain, A., et al: ‘Handbook of fingerprint recognition’ (Springer-Verlag London Limited, 2009, 2nd edn.).

Related content

This is a required field
Please enter a valid email address