BEAT – biometrics evaluation and testing

Marcel, Sébastien

doi:10.1016/s0969-4765(13)70014-6

Cited by 24 publications

(11 citation statements)

References 134 publications

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“…[205]). Interesting existing initiatives in this area are, for example: the BEAT platform [206] with the aim of developing a general standard framework biometric technologies' evaluation, along with several more constrained initiatives such as the indexing competition under Fingerprint Verification Competition (FVC‐onGoing) [207], the 1:N Evaluation under Face Recognition Vendor Test (FRVT) [208], one‐to‐many evaluations under Iris Exchange – for fingerprint, face, and iris characteristics, respectively.…”

Section: Discussionmentioning

confidence: 99%

Computational workload in biometric identification systems: an overview

2019

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Computational workload is one of the key challenges in biometric identification systems. The naïve retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems have been developed. In this article, a taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware-based acceleration. Lastly, the pertinent technical considerations and trade-offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.

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Section: Discussionmentioning

confidence: 99%

Computational workload in biometric identification systems: an overview

2019

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“…The challenge of PAD was addressed by multiple international research projects, such as Tabula Rasa [ 6 ], BEAT [ 7 ], Odin [ 8 ], and RESPECT [ 9 ]. Additionally, the LivDet challenges [ 10 , 11 ] have invited researchers from academia and industry to benchmark their PAD algorithms on identical datasets since 2009.…”

Section: Introductionmentioning

confidence: 99%

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Kolberg

Gläsner

Breithaupt

et al. 2021

Sensors

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Within the last few decades, the need for subject authentication has grown steadily, and biometric recognition technology has been established as a reliable alternative to passwords and tokens, offering automatic decisions. However, as unsupervised processes, biometric systems are vulnerable to presentation attacks targeting the capture devices, where presentation attack instruments (PAI) instead of bona fide characteristics are presented. Due to the capture devices being exposed to the public, any person could potentially execute such attacks. In this work, a fingerprint capture device based on thin film transistor (TFT) technology has been modified to additionally acquire the impedances of the presented fingers. Since the conductance of human skin differs from artificial PAIs, those impedance values were used to train a presentation attack detection (PAD) algorithm. Based on a dataset comprising 42 different PAI species, the results showed remarkable performance in detecting most attack presentations with an APCER = 2.89% in a user-friendly scenario specified by a BPCER = 0.2%. However, additional experiments utilising unknown attacks revealed a weakness towards particular PAI species.

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“…Given the importance of increasing the robustness of biometric systems to these attacks, and hence the systems' security, this area of research has attracted a considerable attention within the biometric community in the last decade. In fact, several international projects like the European Tabula Rasa [70] and BEAT [48], or the more recent US Odin research program [55], deal with these security concerns. In addition, the LivDet liveness detection competition series on iris [79] and fingerprint [80] have been running since 2009.…”

Section: Introductionmentioning

confidence: 99%

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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BEAT – biometrics evaluation and testing

Cited by 24 publications

References 134 publications

Computational workload in biometric identification systems: an overview

Computational workload in biometric identification systems: an overview

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Presentation Attack Detection for Finger Recognition

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