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Enhanced Residual Orientation for Improving Fingerprint Quality

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Book cover Computer Vision Systems (ICVS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9163))

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Abstract

Fingerprint possesses unique, hard to lose, and reliable characteristics. In the recent years, it has been widely applied in biometrics. However, in fingerprint identification, blurred images often occur owing to uneven pressing force; and result in recognition errors. This study proposes an innovative fingerprint quality improvement algorithm to enhance the contrast of fingerprint image and to reduce blurs. By employing D4 discrete wavelet transformation, images are transformed from spatial domain to four frequency domain sub-bands. Then interactive compensation is performed on each band through the multi-resolution characteristic of wavelet transformation and singular value decomposition. Finally, compensated images are reconstructed through inverse-wavelet transformation. After going through our developed fuzzy fingerprint detection system, the fuzzy extent of compensated images can be effectively improved for later backend identification. This study employed NIST-4 and FVC fingerprint databases. The experimental results showed that our method actually could effectively improve blurs in fingerprint.

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Acknowledgment

The authors would like to acknowledge the support received from MOST through project number 103-2221-E-151-037.

Author information

Authors and Affiliations

  1. Institute of Photonics and Communications, National Kaohsiung University of Applied Sciences, Kaohsing, 807, Taiwan

    Jing-Wein Wang & Ngoc Tuyen Le

  2. Computer Science and Information Engineering, Taipei Chengshih University of Science and Technology, Taipei, 112, Taiwan

    Tzu-Hsiung Chen

Authors
  1. Jing-Wein Wang
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  2. Ngoc Tuyen Le
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  3. Tzu-Hsiung Chen
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Corresponding author

Correspondence to Jing-Wein Wang .

Editor information

Editors and Affiliations

  1. Aalborg University, Copenhagen, Denmark

    Lazaros Nalpantidis

  2. Aalborg University, Copenhagen, Denmark

    Volker Krüger

  3. Royal Institute of Technology - KTH, Stockholm, Sweden

    Jan-Olof Eklundh

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Gasteratos

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© 2015 Springer International Publishing Switzerland

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Wang, JW., Le, N.T., Chen, TH. (2015). Enhanced Residual Orientation for Improving Fingerprint Quality. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-20904-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20903-6

  • Online ISBN: 978-3-319-20904-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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