Cargando…
Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets
Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devi...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037165/ https://www.ncbi.nlm.nih.gov/pubmed/33805005 http://dx.doi.org/10.3390/s21072248 |
_version_ | 1783677079829610496 |
---|---|
author | Kauba, Christof Söllinger, Dominik Kirchgasser, Simon Weissenfeld, Axel Fernández Domínguez, Gustavo Strobl, Bernhard Uhl, Andreas |
author_facet | Kauba, Christof Söllinger, Dominik Kirchgasser, Simon Weissenfeld, Axel Fernández Domínguez, Gustavo Strobl, Bernhard Uhl, Andreas |
author_sort | Kauba, Christof |
collection | PubMed |
description | Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included. |
format | Online Article Text |
id | pubmed-8037165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80371652021-04-12 Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets Kauba, Christof Söllinger, Dominik Kirchgasser, Simon Weissenfeld, Axel Fernández Domínguez, Gustavo Strobl, Bernhard Uhl, Andreas Sensors (Basel) Article Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included. MDPI 2021-03-24 /pmc/articles/PMC8037165/ /pubmed/33805005 http://dx.doi.org/10.3390/s21072248 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Kauba, Christof Söllinger, Dominik Kirchgasser, Simon Weissenfeld, Axel Fernández Domínguez, Gustavo Strobl, Bernhard Uhl, Andreas Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets |
title | Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets |
title_full | Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets |
title_fullStr | Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets |
title_full_unstemmed | Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets |
title_short | Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets |
title_sort | towards using police officers’ business smartphones for contactless fingerprint acquisition and enabling fingerprint comparison against contact-based datasets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037165/ https://www.ncbi.nlm.nih.gov/pubmed/33805005 http://dx.doi.org/10.3390/s21072248 |
work_keys_str_mv | AT kaubachristof towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets AT sollingerdominik towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets AT kirchgassersimon towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets AT weissenfeldaxel towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets AT fernandezdominguezgustavo towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets AT stroblbernhard towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets AT uhlandreas towardsusingpoliceofficersbusinesssmartphonesforcontactlessfingerprintacquisitionandenablingfingerprintcomparisonagainstcontactbaseddatasets |