Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks

Gonzalez-Soler, Lazaro J.; Gomez‐Barrero, Marta; Chang, Leonardo; Pérez-Suárez, Airel; Busch, Christoph

doi:10.1109/access.2020.3048756

Cited by 46 publications

(31 citation statements)

References 40 publications

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“…For this purpose, the SVM [ 52 ] is used as a classifier, since it constantly achieved remarkable performances across different fingerprint PAD studies [ 34 , 35 , 53 , 54 , 55 , 56 , 57 ]. The SVM is designed to work on high-dimensional input data and derive binary decisions by defining a hyperplane that separates both classes.…”

Section: Presentation Attack Detection Methodsmentioning

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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Section: Presentation Attack Detection Methodsmentioning

confidence: 99%

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Kolberg

Gläsner

Breithaupt

et al. 2021

Sensors

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“…Therefore, to assign a new point to a cluster, K-means calculates its distance with the closest centroids. The K-means computational complexity is O(kN ) and due to its rapid convergence, this clustering algorithm has been widely used in numerous computer vision and pattern recognition tasks [58]- [60]. In our work, the centroids represent a codebook for a particular sub-space.…”

Section: K-meansmentioning

confidence: 99%

Stable Hash Generation for Efficient Privacy-Preserving Face Identification

Osorio-Roig

Rathgeb

Drozdowski

et al. 2022

IEEE Trans. Biom. Behav. Identity Sci.

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The development of large-scale facial identification systems that provide privacy protection of the enrolled subjects represents an open challenge. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes appear to be unsuitable for indexing (workload reduction) in biometric identification systems. More precisely, they have been utilised in identification systems performing exhaustive searches, thereby leading to degradations of the computational efficiency. In this work, we propose a privacypreserving face identification system which utilises a Product Quantisation-based hash look-up table for indexing and retrieval of protected face templates. These face templates are protected through fully homomorphic encryption schemes, thereby guaranteeing high privacy protection of the enrolled subjects. For the best configuration, the experimental evaluation carried out over closed-set and open-set settings shows the feasibility of the proposed technique for the use in large-scale facial identification systems: a workload reduction down to 0.1% of a baseline approach performing an exhaustive search is achieved together with a low pre-selection error rate of less than 1%. In terms of biometric performance, a False Negative Identification Rate (FNIR) in range of 0.0% -0.2% is obtained for practical False Positive Identification Rate (FPIR) values on the FEI and FERET face databases. In addition, our proposal shows competitive performance on unconstrained databases, e.g., the LFW face database. To the best of the authors' knowledge, this is the first work presenting a competitive privacy-preserving workload reduction scheme which performs template comparisons in the encrypted domain.

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“…The test sample is then processed in the same way and the classifier validates whether the current sample is similar enough to the ones seen during training. The idea is that PA samples differ from bona fide ones [11] 2016 One-class SVMs + score fusion [12] CNNs, unknown PAs, cross sensor/DB [13] 2019 One-class GANs [14] Patch-based LSTM + CNN, LOO [15] Fingerprint Spoof Buster, LOO + best subset [16,17] Feature encoding, unknown PAs, cross sensor/DB [18] 2020 CNN, ARL, unknown PAs, cross sensor/DB [19] One-class convolutional autoencoder [20] and thus can be detected. Using one-class support vector machines (SVMs), Ding and Ross [12] trained on twelve different feature sets of the LivDet 2011 database [23].…”

Section: Related Workmentioning

confidence: 99%

On the generalisation capabilities of fingerprint presentation attack detection methods in the short wave infrared domain

2021

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Nowadays, fingerprint-based biometric recognition systems are becoming increasingly popular. However, in spite of their numerous advantages, biometric capture devices are usually exposed to the public and thus vulnerable to presentation attacks (PAs). Therefore, presentation attack detection (PAD) methods are of utmost importance in order to distinguish between bona fide and attack presentations. Owing to the nearly unlimited possibilities to create new presentation attack instruments (PAIs), unknown attacks are a threat to the existing PAD algorithms. This fact motivates research on generalisation capabilities in order to find PAD methods that are resilient to new attacks. In this context, the authors evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4339 PA samples, including 45 different PAI species. The PAD data is captured in the short wave infrared domain, and the results discuss the advantages and drawbacks of this PAD technique regarding unknown attacks.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks

Cited by 46 publications

References 40 publications

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Stable Hash Generation for Efficient Privacy-Preserving Face Identification

On the generalisation capabilities of fingerprint presentation attack detection methods in the short wave infrared domain

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