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Blurred fingerprint image enhancement: algorithm analysis and performance evaluation

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Abstract

A conventional automatic fingerprint matching process uses similarity score to quantify similarity between fingerprint images to be matched, and the similarity score can be determined with a minutiae extraction algorithm (MEA) which extracts minutiae from fingerprint images. The performance of MEA relies on the quality of fingerprint images. In case of blurred fingerprint images, it becomes difficult to obtain a reliable similarity score. As the result, an image enhancement algorithm should be incorporated with MEA when the fingerprint image is blurred. In this study, Volterra filter is proposed to enhance blurred fingerprints and compared against different enhancement algorithms. Experimental results show that Volterra filter outperforms other techniques such as Laplacian, Wiener, and Gabor filters for enhancing blurred images and its calculation complexity is moderate among techniques considered in this study.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Miami University, Oxford, OH, USA

    Annatoma Arif, Tuo Li & Chi-Hao Cheng

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  1. Annatoma Arif
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  2. Tuo Li
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  3. Chi-Hao Cheng
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Correspondence to Chi-Hao Cheng.

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Arif, A., Li, T. & Cheng, CH. Blurred fingerprint image enhancement: algorithm analysis and performance evaluation. SIViP 12, 767–774 (2018). https://doi.org/10.1007/s11760-017-1218-0

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  • DOI: https://doi.org/10.1007/s11760-017-1218-0

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