SynCoLFinGer: Synthetic contactless fingerprint generator

Priesnitz, Jannis; Rathgeb, Christian; Buchmann, Nicolas; Busch, Christoph

doi:10.1016/j.patrec.2022.04.003

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…Several notable publications have focused on generating synthetic fingerprint images using various algorithms and techniques [10][11][12][13][14][15]. However, a critical distinction lies in the fact that these existing publications primarily concentrate on simulating fingerprint images rather than physically creating synthetic fingerprint targets.…”

Section: Synthetic Fingerprint Generationmentioning

confidence: 99%

Towards Synthetic, Physical Fingerprint Targets

Ruzicka,

Strobl,

Bergmann

et al. 2024

Preprint

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Biometric fingerprint identification hinges on the reliability of its sensors, however, calibrating and standardizing these sensors poses significant challenges, particularly in regard to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like shaped phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels and in the contactless fingerprint sensor setting, 3D printing and silicone filling provides the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

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Section: Synthetic Fingerprint Generationmentioning

confidence: 99%

Towards Synthetic, Physical Fingerprint Targets

Ruzicka,

Strobl,

Bergmann

et al. 2024

Preprint

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show abstract

“…Another advantage is that all available realworld databases remain available for testing purposes. We use the method described in [31] for generating a mobile contactless fingerprint database for the training of MCLFIQ. SynCoLFinGer is a synthetic contactless fingerprint generator which aims to generate samples captured by smartphones.…”

Section: B Training Processmentioning

confidence: 99%

“…Due to the lack of publicly available databases, the contactless fingerprint training database considered in the work is generated synthetically by the SynCoLFinGer method [31]. SynCoLFinGer includes the generation of contactless finger images of distinct quality.…”

Section: A Training Databasementioning

confidence: 99%

MCLFIQ: Mobile Contactless Fingerprint Image Quality

Priesnitz,

Weißenfeld,

Ruzicka

et al. 2024

IEEE Trans. Biom. Behav. Identity Sci.

Self Cite

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We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we retrained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three realworld contactless fingerprint databases using three recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2 method, a sharpness-based quality assessment algorithm developed for contactless fingerprint images and the general purpose image quality assessment method BRISQUE. Furthermore, benchmarks on four contact-based fingerprint datasets are also conducted. Obtained results show that the fine-tuning of NFIQ 2 on synthetic contactless fingerprints is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurately and is more robust compared to all baseline methods on contactless fingerprints. We suggest considering the proposed MCLFIQ method as a starting point for the development of a new standard algorithm for contactless fingerprint quality assessment.

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“…First, master fingerprint is created, then environmental influences are added, then user and finger conditions, then fingerprint damages, and finally sensor characteristics. The study in [35] focuses on modeling contactless fingerprints. Synthetic samples are designed to reflect properties of capturing process, subject characteristics, and environmental influences.…”

Section: Studymentioning

confidence: 99%