CNN Patch--Based Voting for Fingerprint Liveness Detection

Toosi, Amirhosein; Cumani, Sandro; Bottino, A.

doi:10.5220/0006582101580165

Cited by 15 publications

(4 citation statements)

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“…Following this patch-wise trend, Toosi et al tested in [27] the accuracy of AlexNet with data augmentation. For classification, the scores are calibrated using log-likelihood ratios.…”

Section: B Deep Learning For Conventional Sensorsmentioning

confidence: 99%

“…All the aforementioned advances have allowed the deployment of DL architectures in many different fields, including biometric recognition [22], [23]. More specifically, convolutional neural networks (CNNs) and deep belief networks (DBNs) have been used for fingerprint PAD purposes, based either on the complete fingerprint samples [24]- [26] or on a patch-wise manner [27]- [29].…”

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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“…Following this patch-wise trend, Toosi et al tested in [27] the accuracy of AlexNet with data augmentation. For classification, the scores are calibrated using log-likelihood ratios.…”

Section: B Deep Learning For Conventional Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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 publicly available LivDet datasets [18][19][20][21][22][23] established a well known and commonly used foundation for software-based fingerprint PAD development. While early fingerprint PAD algorithms mostly utilised handcrafted feature extractions and classifiers [24][25][26][27][28][29], a shift towards deep learning approaches is noticeable in more recent publications [30][31][32][33]. However, when focussing on unknown attacks and cross-sensor and cross-database scenarios, it is clear that handcrafted methods are able to outperform deep learning approaches, as was shown by the winner of the LivDet 2019 competition [34].…”

Section: Related Workmentioning

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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“…However, despite the many advantages, iris biometric systems are highly susceptible to presentation attacks, usually referred to as spoofing techniques, that attempt to conceal or impersonate other identities [Kohli et al, 2016, Toosi et al, 2017, Pala and Bhanu, 2017, Czajka and Bowyer, 2018,Sajjad et al, 2019,Tolosana et al, 2020. Examples of typical iris spoofing attacks include printed iris images, video playbacks, artificial eyes, and textured contact lenses.…”

Section: Introductionmentioning

confidence: 99%

CNN Hyperparameter Tuning Applied to Iris Liveness Detection

Kimura

Lucio

Britto

et al. 2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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The iris pattern has significantly improved the biometric recognition field due to its high level of stability and uniqueness. Such physical feature has played an important role in security and other related areas. However, presentation attacks, also known as spoofing techniques, can be used to bypass the biometric system with artifacts such as printed images, artificial eyes, and textured contact lenses. To improve the security of these systems, many liveness detection methods have been proposed, and the first Internacional Iris Liveness Detection competition was launched in 2013 to evaluate their effectiveness. In this paper, we propose a hyperparameter tuning of the CASIA algorithm, submitted by the Chinese Academy of Sciences to the third competition of Iris Liveness Detection, in 2017. The modifications proposed promoted an overall improvement, with 8.48% Attack Presentation Classification Error Rate (APCER) and 0.18% Bonafide Presentation Classification Error Rate (BPCER) for the evaluation of the combined datasets. Other threshold values were evaluated in an attempt to reduce the trade-off between the APCER and the BPCER on the evaluated datasets and worked out successfully.

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CNN Patch--Based Voting for Fingerprint Liveness Detection

Cited by 15 publications

References 0 publications

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

CNN Hyperparameter Tuning Applied to Iris Liveness Detection

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