Fingerprint Presentation Attack Detection using Laser Speckle Contrast Imaging

Keilbach, Pascal; Kolberg, Jascha; Gomez‐Barrero, Marta; Busch, Christoph; Langweg, Hanno

doi:10.23919/biosig.2018.8552931

Cited by 26 publications

(25 citation statements)

References 13 publications

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“…However, the development of new countermeasures usually brings the corresponding development of new attacks, in this case, new PAI species. To tackle them, we plan to fuse the techniques developed in this work, which analyse the surface of the finger within the SWIR spectrum, with other approaches analysing bona fide properties below the skin [51], [74].…”

Section: Discussionmentioning

confidence: 99%

“…Finally, Keilbach et al analysed in [51] the PAD capabilities of laser speckle contrast images (LSCI) over a larger database, also acquired within the BATL project and comprising 32 PAIs and more than 750 samples. In this case, several descriptors were extracted from the LSCI sequences, including the well-known local binary patterns (LBP) or the histogram of oriented gradients (HOG).…”

Section: A Non-conventional Fingerprint Sensorsmentioning

confidence: 99%

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Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Tolosana

Gomez‐Barrero

Busch

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, Presentation Attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. The biometrics community has thus made a considerable effort to the development of automatic Presentation Attack Detection (PAD) mechanisms, for instance through the international LivDet competitions.In this context, we present a novel fingerprint PAD scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the-art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.36% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).

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Section: Discussionmentioning

confidence: 99%

Section: A Non-conventional Fingerprint Sensorsmentioning

confidence: 99%

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Tolosana

Gomez‐Barrero

Busch

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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“…Lastly, in a subsequent work by Gomez-Barrero et al [31], the SWIR CNN approaches proposed in [72] are combined with an enhancement of the handcrafted features extracted from the LSCI data in [43]. This combined approach, tested on the same database comprising 35 different PAI species, shows a clear improvement on the detection capabilities of the proposed method, even if only 2 sets of images are used (i.e.…”

Section: Fingerprint Presentation Attack Detectionmentioning

confidence: 92%

“…Additionally, it has been shown that Laser Speckle Contrast Imaging (LSCI) can be used for PAD purposes [43]. The LSCI technique comes from biomedical applications, where it has been applied to visualise and monitor microvascular blood flow in biological tissues, such as skin and retina [65].…”

Section: Fingerprint Presentation Attack Detectionmentioning

confidence: 99%

“…The LSCI technique comes from biomedical applications, where it has been applied to visualise and monitor microvascular blood flow in biological tissues, such as skin and retina [65]. Keilbach et al capture the blood movement beneath the skin to differentiate living fingers from presentation attacks in [43]. However, the utilised laser also penetrates thin transparent fingerprint overlays, thereby detecting the underlying blood flow and falsely classifying the presentation as a bona fide one.…”

Section: Fingerprint Presentation Attack Detectionmentioning

confidence: 99%

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Presentation Attack Detection for Finger Recognition

Kolberg

Gomez‐Barrero

Venkatesh

et al. 2019

Advances in Computer Vision and Pattern Recognition

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Whereas other biometric characteristics, such as the face, are readily available for an eventual attacker through social media or easy to capture with a conventional smartphone, vein patterns can only be acquired with dedicated sensors. This fact makes them relevant not only for recognition purposes but especially for Presentation Attack Detection (PAD), for instance, in combination with fingerprint recognition. In this chapter, we make use of this combination and present a finger vein-based PAD algorithm to detect presentation attacks targeting fingerprint recognition. The experiments are carried out on a newly collected database, comprising 32 species of Presentation Attack Instruments ranging from printed artefacts to more sophisticated fingerprint overlays. The results show that our method preserves a convenient usage while detecting around 90% of the attacks. However, thin and transparent fingerprint overlays remain very challenging.

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