Abstract
Traditional fingerprint methods based on minutiae matching perform well for the acquisition of large area fingerprint. But the accuracy rate and the robustness of small area fingerprint decreases obviously when contains less minutia. Aiming at solving the above problem, a small area fingerprint matching method based on Convolution Neural Network (CNN) which selecting the center block of fingerprint as the region of interest (ROI) after preprocessing and using the Gabor filter to extract feature as multidimensional feature extension named ROIFE_CNN (ROI of fingerprint feature extension recognition of CNN) is proposed to enhance robustness. Experiments show that the accuracy of small area fingerprint classification based on CNN is enhanced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhai, L., Hu, Q.: The research of double-biometric identification technology based on finger geometry & palm print. In: 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 3530–3533. IEEE (2011)
Fernandez-Saavedra, B., Sanchez-Reillo, R., Ros-Gomez, R., et al.: Small fingerprint scanners used in mobile devices: the impact on biometric performance. Iet Biom. 5(1), 28–36 (2016)
Mathur, S., Vjay, A., Shah, J., et al.: Methodology for partial fingerprint enrollment and authentication on mobile devices. In: International Conference on Biometrics, 1–8. IEEE (2016)
Lee, H.C., Gaensslen, R.E.: Advances in Fingerprint Technology. Elsevier, New York (2001)
Maio, D., Maltoni, D.: Direct gray-scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 27–40 (1997)
Jiang, X., Yau, W.Y., Ser, W.: Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge. Pattern Recogn. 34(5), 999–1013 (2001)
Ratha, N.K., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer, New York (2003). https://doi.org/10.1007/b97425
Schulingkamp, R.J., Pagano, T.C., Hung, D., et al.: Insulin receptors and insulin action in the brain: review and clinical implications. Neurosci. Biobehav. Rev. 24(8), 855–872 (2000)
Liu, Y., Ma, Y., Feng, X., et al.: Fingerprint identification preprocessing algorithms based on gabor filter. Comput. Meas. Control 1, 48 (2007)
Girgis, M.R., Sewisy, A.A., Mansour, R.F.: A robust method for partial deformed fingerprints verification using genetic algorithm. Expert Syst. Appl. 36(2), 2008–2016 (2009)
Zhao, Q., Zhang, D., Zhang, L., et al.: High resolution partial fingerprint alignment using pore–valley descriptors. Pattern Recogn. 43(3), 1050–1061 (2010)
Wang, Y.A., Hu, J.: Global ridge orientation modeling for partial fingerprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 72–87 (2010)
Selvarani, S., Jebapriya, S., Mary, R.S.: Automatic identification and detection of altered fingerprints. In: 2014 International Conference on Intelligent Computing Applications (ICICA), pp. 239–243. IEEE (2014)
Nakashika, T., Garcia, C., Takiguchi, T.: Local-feature-map integration using convolutional neural networks for music genre classification (2012)
Lavin, A.: maxDNN: an efficient convolution kernel for deep learning with maxwell GPUs. Computer Science (2015)
Acknowledgement
This research is supported by National Key R&D Program of China (No. 2016YFB0800201), National Natural Science Foundation of China (No. 61772162), Zhejiang Natural Science Foundation of China (No. LY16F020016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, J., Wu, Z., Zhang, J. (2018). A Robust Fingerprint Identification Method by Deep Learning with Gabor Filter Multidimensional Feature Expansion. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-00012-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00011-0
Online ISBN: 978-3-030-00012-7
eBook Packages: Computer ScienceComputer Science (R0)