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A fingerprint identification algorithm by clustering similarity

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Abstract

This paper introduces a fingerprint identification algorithm by clustering similarity with the view to overcome the dilemmas encountered in fingerprint identification. To decrease multi-spectrum noises in a fingerprint, we first use a dyadic scale space (DSS) method for image enhancement. The second step describes the relative features among minutiae by building a minutia-simplex which contains a pair of minutiae and their local associated ridge information, with its transformation-variant and invariant relative features applied for comprehensive similarity measurement and for parameter estimation respectively. The clustering method is employed to estimate the transformation space. Finally, multi-resolution technique is used to find an optimal transformation model for getting the maximal mutual information between the input and the template features. The experimental results including the performance evaluation by the 2nd International Verification Competition in 2002 (FVC2002), over the four fingerprint databases of FVC2002 indicate that our method is promising in an automatic fingerprint identification system (AFIS).

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Authors and Affiliations

  1. Center for Biometric Research and Testing (CBRT), Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 100080, Beijing, China

    Tian Jie, He Yuliang, Chen Hong & Yang Xin

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  1. Tian Jie
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  2. He Yuliang
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  3. Chen Hong
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  4. Yang Xin
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Correspondence to Tian Jie.

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Tian, J., He, Y., Chen, H. et al. A fingerprint identification algorithm by clustering similarity. Sci China Ser F 48, 437–451 (2005). https://doi.org/10.1360/04yf0113

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  • DOI: https://doi.org/10.1360/04yf0113

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