General Requirements on Synthetic Fingerprint Images for Biometric Authentication and Forensic Investigations

Makrushin, Andrey; Kauba, Christof; Kirchgasser, Simon; Seidlitz, Stefan; Kraetzer, Christian; Uhl, Andreas; Dittmann, Jana

doi:10.1145/3437880.3460410

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…However, a commonly agreed methodology on how to practically assess the quality and utility of synthetic biometric samples still does not exist. In our previous study [24], we propose seven requirements on synthetic biometric samples and/or generative models on example of synthetic fingerprint images:…”

Section: General Requirements On Synthetic Samplesmentioning

confidence: 99%

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

2023

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Section: General Requirements On Synthetic Samplesmentioning

confidence: 99%

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

2023

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“…We conducted a literature study by searching for "requirements" + "biometric" at major publishers. This includes (Srivastava, 2013;Zirjawi et al, 2015;Schiavone et al, 2016;Alt and Schneegass, 2022;Makrushin et al, 2021;Mahfouz et al, 2017). We then extracted the following main requirements:…”

Section: Biometric Authenticationmentioning

confidence: 99%

Evaluation Scheme to Analyze Keystroke Dynamics Methods

Dimaratos

Pöhn

2023

Proceedings of the 9th International Conference on Information Systems Security and Privacy

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Password authentication is a weak point for security as passwords are easily stolen and a user may ignore the security by using a simple password. Therefore, services increasingly demand a second factor. While this may enhance security, it comes with a lower level of usability and another factor to be forgotten. A smartphone is an important device in daily life. With the growing number of sensors and features in a smartphone, keystroke dynamics may provide an easy-to-use method. In this paper, we introduce requirements for biometric authentication and keystroke dynamics. This results in an evaluation scheme, which is applied to three selected approaches. Based on the comparison, keystroke dynamics and the evaluation scheme are discussed. The obtained results indicate that keystroke dynamics can be used as another authentication method but can be bypassed by stronger adversaries. For further research, a common data set would improve the comparability.

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“…In our previous paper [7], we have demonstrated that the pix2pix architecture is a powerful and scalable conditional GAN (cGAN) that fits well for reconstructing fingerprint images from minutiae. Reconstruction from minutiae is also a key for fulfilling the requirements on the anonymity and diversity (as these are introduced in [8]) of synthetic fingerprints because the minutiae sets can be created in a pseudo-random way or simply derived from synthetic fingerprints that are known to be anonymous.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms

Makrushin,

Mannam,

Dittmann

2023

Applied Sciences

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The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success.

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General Requirements on Synthetic Fingerprint Images for Biometric Authentication and Forensic Investigations

Cited by 12 publications

References 13 publications

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Evaluation Scheme to Analyze Keystroke Dynamics Methods

Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms

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