Review of the Fingerprint Liveness Detection (LivDet) Competition Series: From 2009 to 2021

Micheletto, Marco; Orrù, Giulia; Casula, Roberto; Yambay, David; Marcialis, Gian Luca; Schuckers, Stephanie

doi:10.1007/978-981-19-5288-3_3

Cited by 15 publications

(11 citation statements)

References 25 publications

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“…The proposed experimental analysis was performed on LivDet 2019 and 2021 datasets [29]. Both datasets contain high-resolution fingerprint images, including live and spoof samples.…”

Section: Datasets and Protocolmentioning

confidence: 99%

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusion

Micheletto,

Luca Marcialis

2024

IEEE Trans. Biom. Behav. Identity Sci.

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The assessment of the fingerprint PADs embedded into a comparison system represents an emerging topic in biometric recognition. Providing models and methods for this aim helps scientists, technologists, and companies to simulate multiple scenarios and have a realistic view of the process's consequences on the recognition system. The most recent models aimed at deriving the overall system performance, especially in the sequential assessment of the fingerprint liveness and comparison pointed out a significant decrease in Genuine Acceptance Rate (GAR). In particular, our previous studies showed that PAD contributes predominantly to this drop, regardless of the comparison system used. This paper's goal is to establish a systematic approach for the "trade-off" computation between the gain in Impostor Attack Presentation Accept Rate (IAPAR) and the loss in GAR mentioned above. We propose a formal "trade-off" definition to measure the balance between tackling presentation attacks and the performance drop on genuine users. Experimental simulations and theoretical expectations confirm that an appropriate "trade-off" definition allows a complete view of the sequential embedding potentials.

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“…The proposed experimental analysis was performed on LivDet 2019 and 2021 datasets [29]. Both datasets contain high-resolution fingerprint images, including live and spoof samples.…”

Section: Datasets and Protocolmentioning

confidence: 99%

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusion

Micheletto,

Luca Marcialis

2024

IEEE Trans. Biom. Behav. Identity Sci.

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“…In this context, artificial replicas known as Presentation Attack Instruments (PAIs) are utilized to deceive fingerprint capturing devices by mimicking fingerprint characteristics. These replicas can be crafted from a variety of materials such as gelatin, silicone, Body Double, different types of glue, or latex [37][38][39][40]. When targeting contact-based fingerprint capturing devices, it is essential that the PAI replicates the elasticity of real skin.…”

Section: Physical Fingerprint Generationmentioning

confidence: 99%

“…Additionally, the PAI must meet criteria for persistence, allowing it to be used multiple times while also being easy to assemble and cost-effective. Silicone and Body Double, in particular, are well suited to fulfill these requirements [40].…”

Section: Physical Fingerprint Generationmentioning

confidence: 99%

Toward Synthetic Physical Fingerprint Targets

Ruzicka,

Strobl,

Bergmann

et al. 2024

Sensors

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Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms’ fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

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“…PAIs can be made from a range of materials and can be either artificial or synthetic fingerprint samples. To address the security concerns caused by these attacks, several automated presentation attack detection (PAD) techniques have been developed in recent years [ 5 , 6 , 7 , 8 , 9 , 10 ]. PAD systems are used to determine whether a specimen is from a genuine subject or an artificial copy (PA or artifact).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Approach for Enhancing Biometric Authentication

Ammour,

Bazi,

Alajlan

2023

J. Imaging

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Unimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To address this issue, we propose an enhanced biometric system based on a multimodal approach using two types of biological traits. We propose to combine fingerprint and Electrocardiogram (ECG) signals to mitigate spoofing attacks. Specifically, we design a multimodal deep learning architecture that accepts fingerprints and ECG as inputs and fuses the feature vectors using stacking and channel-wise approaches. The feature extraction backbone of the architecture is based on data-efficient transformers. The experimental results demonstrate the promising capabilities of the proposed approach in enhancing the robustness of the system to presentation attacks.

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Review of the Fingerprint Liveness Detection (LivDet) Competition Series: From 2009 to 2021

Cited by 15 publications

References 25 publications

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusion

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusion

Toward Synthetic Physical Fingerprint Targets

Multimodal Approach for Enhancing Biometric Authentication

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