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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/7/2248 |
| _version_ | 1797540178281103360 |
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| author | Christof Kauba Dominik Söllinger Simon Kirchgasser Axel Weissenfeld Gustavo Fernández Domínguez Bernhard Strobl Andreas Uhl |
| author_facet | Christof Kauba Dominik Söllinger Simon Kirchgasser Axel Weissenfeld Gustavo Fernández Domínguez Bernhard Strobl Andreas Uhl |
| author_sort | Christof Kauba |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T12:57:18Z |
| format | Article |
| id | doaj.art-aa97830d08444f8f8910d1f1b152ee9c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:57:18Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa97830d08444f8f8910d1f1b152ee9c2023-11-21T11:49:41ZengMDPI AGSensors1424-82202021-03-01217224810.3390/s21072248Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based DatasetsChristof Kauba0Dominik Söllinger1Simon Kirchgasser2Axel Weissenfeld3Gustavo Fernández Domínguez4Bernhard Strobl5Andreas Uhl6The Multimedia Signal Processing and Security Lab, University of Salzburg, 5020 Salzburg, AustriaThe Multimedia Signal Processing and Security Lab, University of Salzburg, 5020 Salzburg, AustriaThe Multimedia Signal Processing and Security Lab, University of Salzburg, 5020 Salzburg, AustriaCenter for Digital Safety & Security, AIT Austrian Institute of Technology, 2444 Seibersdorf, AustriaCenter for Digital Safety & Security, AIT Austrian Institute of Technology, 2444 Seibersdorf, AustriaCenter for Digital Safety & Security, AIT Austrian Institute of Technology, 2444 Seibersdorf, AustriaThe Multimedia Signal Processing and Security Lab, University of Salzburg, 5020 Salzburg, AustriaRecent 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.https://www.mdpi.com/1424-8220/21/7/2248mobile biometricsfingerprint recognitionfingertip segmentationreal-time applicationperformance evaluation |
| spellingShingle | Christof Kauba Dominik Söllinger Simon Kirchgasser Axel Weissenfeld Gustavo Fernández Domínguez Bernhard Strobl Andreas Uhl Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets Sensors mobile biometrics fingerprint recognition fingertip segmentation real-time application performance evaluation |
| 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 | mobile biometrics fingerprint recognition fingertip segmentation real-time application performance evaluation |
| url | https://www.mdpi.com/1424-8220/21/7/2248 |
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