Towards Using Police Officers' Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 7; p. 2248
• Main Authors: Kauba, Christof, Söllinger, Dominik, Kirchgasser, Simon, Weissenfeld, Axel, Fernández Domínguez, Gustavo, Strobl, Bernhard, Uhl, Andreas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24-03-2021; MDPI
• Subjects: Algorithms; Automation; Biometric recognition systems; Biometrics; Datasets; Digital cameras; fingerprint recognition; Fingerprint verification; fingertip segmentation; Identification; Image acquisition; mobile biometrics; performance evaluation; Police; real-time application; Smartphones; Usability; performance evaluation; real-time application; fingertip segmentation; fingerprint recognition; mobile biometrics
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21072248

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.