Fingerprint presentation attacks detection based on the user-specific effect

Ghiani, Luca; Marcialis, Gian Luca; Roli, Fabio

doi:10.1109/btas.2017.8272717

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…In last years it was noticed that the presence of the same users both in the train set and in the test set, increased the PAD accuracy of the system. In fact, the existence of artifacts due to the human skin (person-specific) and to the particular curvature of ridges and valleys (finger-specific) can impact in PAD systems' performance [5,6] and can be exploit to improve the integrated system. In particular, the data acquired during the Green Bit 1000 400 400 400 1700 680 680 680 Orcanthus 1000 400 400 400 1700 680 658 680 Digital Persona 999 400 400 399 1700 679 670 679 Table 2: Number of samples for each scanner and each part of the dataset.…”

Section: Proposed Analysismentioning

confidence: 99%

“…In last years it was noticed that the presence of the same users both in the train set and in the test set, increased the PAD accuracy of the system. In fact, the existence of artifacts due to the human skin (person-specific) and to the particular curvature of ridges and valleys (finger-specific) can impact in PAD systems' performance [5,6] and can be exploit to improve the integrated system. In particular, the data acquired during the recognition system's initial enrollment phase can be used to lower the PAD error rate and consequently, to improve the integrated system performance.…”

Section: Proposed Analysismentioning

confidence: 99%

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Analysis of “User-Specific Effect” and Impact of Operator Skills on Fingerprint PAD Systems

Orrù

Tuveri

Ghiani

et al. 2019

New Trends in Image Analysis and Processing – ICIAP 2019

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Fingerprint Liveness detection, or presentation attacks detection (PAD), that is, the ability of detecting if a fingerprint submitted to an electronic capture device is authentic or made up of some artificial materials, boosted the attention of the scientific community and recently machine learning approaches based on deep networks opened novel scenarios. A significant step ahead was due thanks to the public availability of large sets of data; in particular, the ones released during the International Fingerprint Liveness Detection Competition (LivDet). Among others, the fifth edition carried on in 2017, challenged the participants in two more challenges which were not detailed in the official report. In this paper, we want to extend that report by focusing on them: the first one was aimed at exploring the case in which the PAD is integrated into a fingerprint verification systems, where templates of users are available too and the designer is not constrained to refer only to a generic users population for the PAD settings. The second one faces with the exploitation ability of attackers of the provided fakes, and how this ability impacts on the final performance. These two challenges together may set at which extent the fingerprint presentation attacks are an actual threat and how to exploit additional information to make the PAD more effective.Keywords Liveness Detection · User-specific effect · skilled operator.

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Section: Proposed Analysismentioning

confidence: 99%

“…In last years it was noticed that the presence of the same users both in the train set and in the test set, increased the PAD accuracy of the system. In fact, the existence of artifacts due to the human skin (person-specific) and to the particular curvature of ridges and valleys (finger-specific) can impact in PAD systems' performance [5,6] and can be exploit to improve the integrated system. In particular, the data acquired during the recognition system's initial enrollment phase can be used to lower the PAD error rate and consequently, to improve the integrated system performance.…”

Section: Proposed Analysismentioning

confidence: 99%

Analysis of “User-Specific Effect” and Impact of Operator Skills on Fingerprint PAD Systems

Orrù

Tuveri

Ghiani

et al. 2019

New Trends in Image Analysis and Processing – ICIAP 2019

Self Cite

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show abstract

“…Furthermore, recent research shows that partial fingerprints from different users have common features that can be exploited to fool authentication systems (27). Last, liveness detection (i.e., whether a physical fingerprint is real or a spoof copy made from synthetic materials like rubber or silicone) systems perform better when trained on samples from the user whose fingerprints they test on, even when those training fingers are different than the testing fingers (28,29).…”

Section: Introductionmentioning

confidence: 99%

Unveiling intra-person fingerprint similarity via deep contrastive learning

Guo,

Ray,

Izydorczak

et al. 2024

Sci. Adv.

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Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.

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Presentation Attacks in Mobile and Continuous Behavioral Biometric Systems

Neal

2020

Advanced Sciences and Technologies for Security Applications

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Fingerprint presentation attacks detection based on the user-specific effect

Cited by 4 publications

References 19 publications

Analysis of “User-Specific Effect” and Impact of Operator Skills on Fingerprint PAD Systems

Analysis of “User-Specific Effect” and Impact of Operator Skills on Fingerprint PAD Systems

Unveiling intra-person fingerprint similarity via deep contrastive learning

Presentation Attacks in Mobile and Continuous Behavioral Biometric Systems

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