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Fingerprint Pore Extraction

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Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail

Abstract

Sweat pores on fingerprints have proven to be discriminative features and have recently been successfully employed in automatic fingerprint recognition systems (AFRS). It is crucial to extract pores precisely to achieve high recognition accuracy. In this chapter two extraction methods will be given. The first method is based on a dynamic anisotropic pore model, which describes pores more accurately by using orientation and scale parameters. Most of previous pore extraction methods detect pores by using a static isotropic pore model; however, their detection accuracy is not satisfactory due to the limited approximation capability of static isotropic models to various types of pores. An adaptive pore extraction method is then developed based on the dynamic anisotropic pore model. It first divides the fingerprint image into well-defined, ill-posed, and background blocks. According to the dominant ridge orientation and frequency on each foreground block, a local instantiation of appropriate pore model is obtained. Finally, the pores are extracted by filtering the block with the adaptively generated pore model. The second method is a novel coarse-to-fine detection method based on convolutional neural networks (CNN) and logical operation. More specifically, pore candidates are coarsely estimated using logical operation at first; then, coarse pore candidates are further computed through well-trained CNN models; precise pore locations are finally refined by logical and morphological operation. Extensive experiments are performed on high resolution fingerprint databases. The results demonstrate that both the two methods can detect pores accurately and robustly, and consequently improve the fingerprint recognition accuracy of pore- based AFRS.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Feng Liu

  2. College of Computer Science, Sichuan University, Chengdu, China

    Qijun Zhao

  3. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China

    David Zhang

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  1. Feng Liu
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  2. Qijun Zhao
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  3. David Zhang
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Liu, F., Zhao, Q., Zhang, D. (2020). Fingerprint Pore Extraction. In: Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail. Springer, Singapore. https://doi.org/10.1007/978-981-15-4128-5_8

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  • DOI: https://doi.org/10.1007/978-981-15-4128-5_8

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  • Print ISBN: 978-981-15-4127-8

  • Online ISBN: 978-981-15-4128-5

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