LivDet 2015 fingerprint liveness detection competition 2015

Mura, Valerio; Ghiani, Luca; Marcialis, Gian Luca; Roli, Fabio; Yambay, David; Schuckers, Stephanie

doi:10.1109/btas.2015.7358776

Cited by 120 publications

(89 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the Crossmatch sensor in LivDet15 [26], spoof training dataset is fabricated using three spoof materials, namely Ecoflex, Play Doh, and Body Double. To simulate testing our method against a novel target spoof material, we train an AdaIN spoof generator on fingerprint spoofs fabricated using the other two (known) materials to learn an effective style extractor and translator amongst spoofs in those two materials.…”

Section: Training Proceduresmentioning

confidence: 99%

Universal Material Translator: Towards Spoof Fingerprint Generalization

Gajawada

Popli

Chugh

et al. 2019

2019 International Conference on Biometrics (ICB)

View full text Add to dashboard Cite

Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones. However, state of the art spoof detectors do not generalize well on unseen spoof materials. This study proposes a style transfer based augmentation wrapper that can be used on any existing spoof detector and can dynamically improve the robustness of the spoof detection system on spoof materials for which we have very low data. Our method is an approach for synthesizing new spoof images from a few spoof examples that transfers the style or material properties of the spoof examples to the content of bonafide fingerprints to generate a larger number of examples to train the classifier on. We demonstrate the effectiveness of our approach on materials in the publicly available LivDet 2015 dataset and show that the proposed approach leads to robustness to fingerprint spoofs of the target material.

show abstract

Section: Training Proceduresmentioning

confidence: 99%

Universal Material Translator: Towards Spoof Fingerprint Generalization

Gajawada

Popli

Chugh

et al. 2019

2019 International Conference on Biometrics (ICB)

View full text Add to dashboard Cite

show abstract

“…Similar to facial spoofing challenge competition, an iris and fingerprint based live detection competition is open [45]. The most recent report (2015) confirms the issue is still not solved, although some improvements exist [46].…”

Section: Biometrics Attackmentioning

confidence: 99%

Biometrics Systems and Technologies: A survey

Buciu

Gacsádi

2016

INT J COMPUT COMMUN

View full text Add to dashboard Cite

In a nutshell, a biometric security system requires a user to provide some biometric features which are then verified against some stored biometric templates. Nowadays, the traditional password based authentication method tends to be replaced by advanced biometrics technologies. Biometric based authentication is becoming increasingly appealing and common for most of the human-computer interaction devices. To give only one recent example, Microsoft augmented its brand new Windows 10 OS version with the capability of supporting face recognition when the user login in. This chapter does not intend to cover a comprehensive and detailed list of biometric techniques. The chapter rather aims at briefly discussing biometric related items, including principles, definitions, biometric modalities and technologies along with their advantages, disadvantages or limitations, and biometric standards, targeting unfamiliar readers. It also mentions the attributes of a biometric system as well as attacks on biometrics. Important reference sources are pointed out so that the interested reader may gain deeper in-depth knowledge by consulting them.

show abstract

“…According to [8], [9], the system must know how to differentiate either a spoof or an authentic finger by ensuring that the fingerprint is from a live user. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional data can be used to verify if a biometric measure is authentic [10], [11]. Therefore, the review is done to completely understand the properties of fake fingerprint detection approach and to identify any possible area for further research.…”

Section: Introductionmentioning

confidence: 99%