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Touchless Palmprint and Fingerprint Recognition

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Advances in Computing, Informatics, Networking and Cybersecurity

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 289))

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Abstract

Biometric systems based on hand traits captured using touchless acquisition procedures are increasingly being used for the automatic recognition of individuals due to their favorable trade-off between accuracy and acceptability by users. Among hand traits, palmprint and fingerprints are the most studied modalities because they offer higher recognition accuracy than other hand-based traits such as finger texture, knuckle prints, or hand geometry. For capturing palmprints and fingerprints, touchless and less-constrained acquisition procedures have the advantage of mitigating the problems caused by latent prints, dirty sensors, and skin distortions. However, touchless acquisition systems for palmprints and fingerprints face several challenges caused by the need to capture the hand while it is moving and under varying illumination conditions. Moreover, images captured using touchless acquisition procedures tend to exhibit complex backgrounds, nonuniform reflections, and perspective distortions. Recently, methods such as adaptive filtering, three-dimensional reconstruction, local texture descriptors, and deep learning have been proposed to compensate for the nonidealities of touchless acquisition procedures, thereby increasing the recognition accuracy while maintaining high usability. This chapter presents an overview of the various methods reported in the literature for touchless palmprint and fingerprint recognition, describing the corresponding acquisition methodologies and processing methods.

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Notes

  1. 1.

    https://homes.di.unimi.it/genovese/3dpalm/.

  2. 2.

    http://iebil.di.unimi.it/palmnet/index.htm.

  3. 3.

    http://iebil.di.unimi.it/fusionnet/index.htm.

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Acknowledgements

This work was supported in part by the EC within the H2020 Program under projects MOSAICrOWN and MARSAL, by the Italian Ministry of Research within the PRIN program under project HOPE, by the Universitá degli Studi di Milano under project AI4FAO, and by JPMorgan Chase & Co. We thank the NVIDIA Corporation for the GPU donated.

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  1. Department of Computer Science, Università Degli Studi di Milano, Via Celoria 18, 20133, Milan, MI, Italy

    Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri & Fabio Scotti

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  1. Ruggero Donida Labati
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  2. Angelo Genovese
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  4. Fabio Scotti
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Correspondence to Vincenzo Piuri .

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Editors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Petros Nicopolitidis

  2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

    Sudip Misra

  3. Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada

    Laurence T. Yang

  4. Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA

    Bernard Zeigler

  5. Dalian University of Technology, Dalian, China

    Zhaolng Ning

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Donida Labati, R., Genovese, A., Piuri, V., Scotti, F. (2022). Touchless Palmprint and Fingerprint Recognition. In: Nicopolitidis, P., Misra, S., Yang, L.T., Zeigler, B., Ning, Z. (eds) Advances in Computing, Informatics, Networking and Cybersecurity. Lecture Notes in Networks and Systems, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-87049-2_9

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