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A fast and accurate method for detecting fingerprint reference point

  • Recent advances in Pattern Recognition and Artificial Intelligence
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Abstract

Unique and stable reference point is essential for registration and identification in automated fingerprint identification systems. Most existing methods for detecting reference points need to scan the fingerprint image or orientation field pixel by pixel or block by block to confirm a candidate reference point. The inherent complexity of this process makes those methods time-consuming. In this paper, we propose a two-step method to improve the efficiency of detecting reference points by (1) determining the singular point, i.e., the approximate position of the reference point, in a novel fast way; then (2) refining the reference point precisely in the local area of the singular point. In the first step, a walking algorithm is proposed which can walk directly to the singular point without scanning the whole fingerprint image and hence it is extremely fast. Then, in the local area around the singular point, an enhanced method based on mean-shift concept (EMS-based method) is designed to localize the reference point precisely. Experimental results on FVC2000 DB1a and DB2a databases validate that the proposed WEMS (Walking + EMS) method outperforms two state-of-the-art methods in terms of accuracy and efficiency.

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Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (Project Nos. 60970034, 61170287, 61232016) and National Science Foundation of Hunan Province (Grant No. 2jj3069).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Xifeng Guo & En Zhu

  2. State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, China

    Jianping Yin

Authors
  1. Xifeng Guo
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  2. En Zhu
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  3. Jianping Yin
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Correspondence to Xifeng Guo.

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Guo, X., Zhu, E. & Yin, J. A fast and accurate method for detecting fingerprint reference point. Neural Comput & Applic 29, 21–31 (2018). https://doi.org/10.1007/s00521-016-2285-9

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  • DOI: https://doi.org/10.1007/s00521-016-2285-9

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