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Comparative Study of Fingerprints Liveness Detection Using Noise in Ridge Valley Structure, Texture Analysis, and CNN Method

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Proceedings of World Conference on Information Systems for Business Management (ISBM 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 834))

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Abstract

Fingerprint is a more reliable and distinctive biometric. The usage of it in security applications is widespread. However, it is simple to spoof fingerprints obtained from scanners by creating false fingers out of gelatin, fevicol, clay, silicon rubber, play-dough, and moldable plastic. In these situations, it is crucial for the researcher to be able to differentiate among real and prosthetic fingers. The liveness detection of fingerprints is a technique to identify actual fingers acquired by fingerprint scanners for person identification by detecting liveness/physiological indications from fingerprints. Convolutional neural networks (CNNs) were employed in this study to decide the liveness of a fingerprint. When compared to the best previously reported findings, our top model obtains total rate of 95% of properly categorized samples.

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Authors and Affiliations

  1. Dr. Vishwanath Karad MIT World Peace University, Pune, India

    Rupali Kute, Alwin Anuse & Bhakti Paranjape

Authors
  1. Rupali Kute
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  2. Alwin Anuse
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  3. Bhakti Paranjape
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Corresponding author

Correspondence to Rupali Kute .

Editor information

Editors and Affiliations

  1. University of Cantabria, Santander, Spain

    Andres Iglesias

  2. University of Aizu, Fukushima, Japan

    Jungpil Shin

  3. Knowledge Chamber of Commerce and Industry, Ahmedabad, Gujarat, India

    Bharat Patel

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kute, R., Anuse, A., Paranjape, B. (2024). Comparative Study of Fingerprints Liveness Detection Using Noise in Ridge Valley Structure, Texture Analysis, and CNN Method. In: Iglesias, A., Shin, J., Patel, B., Joshi, A. (eds) Proceedings of World Conference on Information Systems for Business Management. ISBM 2023. Lecture Notes in Networks and Systems, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-99-8349-0_19

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