Abstract
Fingerprint orientation field, representing the fingerprint ridge-valley structure direction, plays an essential role in fingerprint preprocessing tasks. Orientation field is able to be reconstructed by either non-parameterized or parameterized methods. In this paper, we propose a new parameterized approach for orientation field modeling. The proposed algorithm minimizes a composite model including three constraints corresponding to a least square data fitting term, a total variation regularization and a \(L_1\) sparse regularization. This model has been shown to be very effective for fingerprint orientation field reconstruction. Furthermore, its effectiveness has been proven by several experiments. First, the experiments on poor-quality fingerprint images are conducted. Visual comparisons demonstrate the robustness of the proposed method when processing noisy fingerprint images. Then, as another application of the proposed model, its resultant sparse representation is employed for fingerprint indexing. The experiments on FVC 2000 DB2a and FVC 2002 DB1a datasets show the superior performance of the proposed model for fingerprint indexing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 777–789 (1998)
Chikkerur, S., Cartwright, A.N., Govindaraju, V.: Fingerprint enhancement using STFT analysis. Pattern Recognition 40(1), 198–211 (2007)
Wang, Y., Hu, J., Phillips, D.: A fingerprint orientation model based on 2D fourier expansion (FOMFE) and its application to singular point detection and fingerprint indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 573–585 (2007)
Wang, Y., Hu, J.: Global ridge orientation modelling for partial fingerprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1), 72–87 (2011)
Zhou, W., Hu, J., Petersen, I., Bennamoun, M.: Partial fingerprint reconstruction with improved smooth extension. In: Lopez, J., Huang, X., Sandhu, R. (eds.) NSS 2013. LNCS, vol. 7873, pp. 756–762. Springer, Heidelberg (2013)
Feng, J., Zhou, J., Jain, A.K.: Orientation field estimation for latent fingerprint enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(4), 925–940 (2013)
Ram, S., Bischof, H., Birchbauer, J.: Modeling fingerprint ridge orientation using Legendre polynomials. Pattern Recognition 43(1), 342–357 (2010)
Liu, M., Yap, P.T.: Invariant representation of orientation fields for fingerprint indexing. Pattern Recognition 45(7), 2532–2542 (2012)
Liu, M., Liu, S., Zhao, Q.: Fingerprint orientation field reconstruction by weighted discrete cosine transform. Information Sciences 268, 65–77 (2014)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Hand book of fingerprint recognition. Springer Verlag (2009)
Tropp, J.: Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory 50(10), 2231–2242 (2004)
Zhou, J., Gu, J.: A model-based method for the computation of fingerprints’ orientation field. IEEE Transactions on Image Processing 13(6), 821–835 (2004)
Wang, Y., Hu, J., Han, F.: Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Applied Mathematics and Computation 185, 823–833 (2007)
Zhou, W., Hu, J., Wang, S., Petersen, I., Bennamoun, M.: Fingerprint indexing based on combination of novel minutiae triplet features. In: Au, M.H., Carminati, B., Kuo, C.-C.J. (eds.) NSS 2014. LNCS, vol. 8792, pp. 377–388. Springer, Heidelberg (2014)
Huang, J., Zhang, S., Metaxas, D.: Efficient MR image reconstruction for compressed MR images. Medical Image Analysis 15, 670–679 (2011)
Amir, B., Marc, T.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing 18(11), 2419–2434 (2009)
Yuan, B., Su, F., Cai, A.: Fingerprint retrieval approach based on novel minutiae triplet features. In: IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 170–175 (2012)
Iloanusi, O., Gyaourova, A., Ross, A.: Indexing fingerprints using minutiae quadruplets. In: IEEE International Conference on Computer Vision and Pattern Recognition Workshops, pp. 127–133 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, J., Hu, J. (2015). Multi-constrained Orientation Field Modeling and Its Application for Fingerprint Indexing. In: Qiu, M., Xu, S., Yung, M., Zhang, H. (eds) Network and System Security. NSS 2015. Lecture Notes in Computer Science(), vol 9408. Springer, Cham. https://doi.org/10.1007/978-3-319-25645-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-25645-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25644-3
Online ISBN: 978-3-319-25645-0
eBook Packages: Computer ScienceComputer Science (R0)