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Fingerprint Image Quality Assessment and Scoring

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Mining Intelligence and Knowledge Exploration (MIKE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

Fingerprint quality estimation is an essential step for eliminating poor quality fingerprint images which can degrade the recognition performance of automatic fingerprint identification system (AFIS). A quality assessment technique along with fingerprint quality score will enable AFIS system to make appropriate decision regarding rejecting the low quality image and recapture a better quality fingerprint image. In this paper, we propose an effective method for evaluating fingerprint image quality (dry, normal dry, good, normal wet and wet) on a local level (block-wise). Feature vector for evaluating fingerprint quality covers moisture, mean, variance, ridge valley area uniformity and ridge line count. Block-wise quality label is assigned through pattern classification based on these features. In addition to quality labels, our proposed method also provides a quality score for a fingerprint image. Manually labeled dry, normal dry, good, normal wet and wet quality blocks of FVC 2004 \(DB1\_a \) dataset is used to create a classification model using decision tree classifier. Block classification accuracy of 95.20% is achieved. Further, the same classification model is utilized to compute overall quality score of a fingerprint image. It has been observed that the overall quality score is accurate according to the manually labeled fingerprint image and also through visual inspection.

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Acknowledgment

The authors are thankful to Science and Engineering Research Board (SERB), DST (ECR/2017/000027), Govt. of India for providing financial support to carry out this research work.

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Authors and Affiliations

  1. Indian Institute of Technology, Indore, India

    Ram Prakash Sharma & Somnath Dey

Authors
  1. Ram Prakash Sharma
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  2. Somnath Dey
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Corresponding author

Correspondence to Ram Prakash Sharma .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Sharma, R.P., Dey, S. (2017). Fingerprint Image Quality Assessment and Scoring. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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