LivDet 2017 Fingerprint Liveness Detection Competition 2017

Mura, Valerio; Orrù, Giulia; Casula, Roberto; Sibiriu, Alessandra; Loi, Giulia; Tuveri, Pierluigi; Ghiani, Luca; Marcialis, Gian Luca

doi:10.1109/icb2018.2018.00052

Cited by 65 publications

(57 citation statements)

References 3 publications

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“…The detection performance was evaluated on LivDet 2011 to 2015, achieving a remarkable ACER of 0.96% on average. However, the ACER increased to 2.0% for a self-acquired database, comprising a larger number of PAIs (12).…”

Section: B Deep Learning For Conventional Sensorsmentioning

confidence: 95%

“…It should be noted that, in addition to the metrics defined in Sect. II two different metrics are used in the LivDet competitions [11], [12]. The Average Classification Error Rate (ACER) is defined as the average of the APCER and the BPCER for a pre-defined decision threshold δ:…”

Section: Related Workmentioning

confidence: 99%

“…Referred to as presentation attack detection (PAD) methods, research in this area has been recently funded by several international projects like the European Tabula Rasa [8] and BEAT [9], or the more recent US ODIN research program [10]. Together with the organisation of the LivDet -liveness detection competition series on iris and fingerprint [11], [12], where the number of participants has been increasing year after year (up to 17 algorithms submitted in 2017), these initiatives have fostered a considerable number of publications on PAD for different biometric characteristics, including iris [13], fingerprint [14], [15], face [16], or handwritten signature [17].…”

Section: Introductionmentioning

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: B Deep Learning For Conventional Sensorsmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Tolosana

Gomez‐Barrero

Busch

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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“…LivDet 2017 [4] consisted of data from three fingerprint sensors: Green Bit DactyScan84C, Orcanthus Certis2 Image and Digital Persona U.are.U 5160. It is composed of almost 6000 images for each scanner.…”

Section: Dataset and Experimental Protocolmentioning

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

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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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“…2) LivDet Datasets: LivDet 2017 [53] dataset is one of the most recent 11 publicly-available LivDet datasets, containing over 17, 500 fingerprint images. These images are acquired using three different fingerprint readers, namely Green Bit, Orcanthus, and Digital Persona.…”

Section: A Datasetsmentioning

confidence: 99%

Fingerprint Spoof Detector Generalization

Chugh

Jain

2021

IEEE Trans.Inform.Forensic Secur.

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We present a style-transfer based wrapper, called Universal Material Generator (UMG), to improve the generalization performance of any fingerprint spoof detector against spoofs made from materials not seen during training. Specifically, we transfer the style (texture) characteristics between fingerprint images of known materials with the goal of synthesizing fingerprint images corresponding to unknown materials, that may occupy the space between the known materials in the deep feature space. Synthetic live fingerprint images are also added to the training dataset to force the CNN to learn generative-noise invariant features which discriminate between lives and spoofs. The proposed approach is shown to improve the generalization performance of a state-of-the-art spoof detector, namely Fingerprint Spoof Buster, from TDR of 75.24% to 91.78% @ FDR = 0.2%. These results are based on a large-scale dataset of 5,743 live and 4,912 spoof images fabricated using 12 different materials. Additionally, the UMG wrapper is shown to improve the average cross-sensor spoof detection performance from 67.60% to 80.63% when tested on the LivDet 2017 dataset. Training the UMG wrapper requires only 100 live fingerprint images from the target sensor, alleviating the time and resources required to generate large-scale live and spoof datasets for a new sensor. We also fabricate physical spoof artifacts using a mixture of known spoof materials to explore the role of cross-material style transfer in improving generalization performance.

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LivDet 2017 Fingerprint Liveness Detection Competition 2017

Cited by 65 publications

References 3 publications

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

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

Fingerprint Spoof Detector Generalization

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