One-Class Fingerprint Presentation Attack Detection Using Auto-Encoder Network

Liu, Feng; Liu, Haozhe; Zhang, Wentian; Liu, Guojie; Shen, Linlin

doi:10.1109/tip.2021.3052341

Cited by 27 publications

(16 citation statements)

References 34 publications

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“…In another approach, Kolberg et al [ 46 ] proposed a convolutional autoencoder for multi-spectral images from a camera-based capture device. In a similar fashion, Liu et al [ 48 ] evaluated an autoencoder on samples acquired with an optical coherence tomography (OCT) sensor.…”

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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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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“…In recent years, with the increasing use of mobile payment, online banking, and other applications, a large number of smart devices are growing exponentially [1][2][3][4]. Due to the security requirements, more and more smart devices tend to use Automated Fingerprint Recognition Systems for personal authentication [2]. However, AFRSs are vulnerable to malicious attacks, such as 2D or 3D artificial fingerprints made of gelatin, silicone, etc.…”

Section: Introductionmentioning

confidence: 99%

“…However, AFRSs are vulnerable to malicious attacks, such as 2D or 3D artificial fingerprints made of gelatin, silicone, etc. [2,[5][6][7][8][9]. To ensure that the user has a secure environment against presentation attacks (PA), lots of PAD methods have been proposed to solve this problem [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…hardware and software based approaches [11,15]. For hardware approaches, special sensors such as imaging technologies based on multispectral, short-wave infrared and, optical coherent tomography (OCT), have been developed to extract live features like odor, blood flow and, heartbeat to discriminate the live samples from spoof samples [2,5,[16][17][18][19][20][21][22][23][24][25]. For instance, a spectroscopy-based device proposed by Nixon et al [21] can obtain the spectral features of the fingertip, which has been proven to effectively distinguish between spoof and live fingertip.…”

Section: Introductionmentioning

confidence: 99%

“…Ultrasonic fingerprint sensors were used by Agassy et al [23] to determine whether the finger is a spoof fingertip or not. Liu et al [2] proposed a One-Class framework for PAD using OCT. As only bonafide is required for training, this method not only has better generalization ability but also alleviates the data dependence problem. However, the aforementioned methods are sensor-dependent and they require additional hardware devices such as OCT-based devices and Raspireader [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

Liu,

Kong,

Liu

et al. 2021

Preprint

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Due to the diversity of attack materials, fingerprint recognition systems (AFRSs) are vulnerable to malicious attacks. It is of great importance to propose effective Fingerprint Presentation Attack Detection (PAD) methods for the safety and reliability of AFRSs. However, current PAD methods often have poor robustness under new attack materials or sensor settings. This paper thus proposes a novel Channel-wise Feature Denoising fingerprint PAD (CFD-PAD) method by considering handling the redundant "noise" information which ignored in previous works. The proposed method learned important features of fingerprint images by weighting the importance of each channel and finding those discriminative channels and "noise" channels. Then, the propagation of "noise" channels is suppressed in the feature map to reduce interference. Specifically, a PA-Adaption loss is designed to constrain the feature distribution so as to make the feature distribution of live fingerprints more aggregate and spoof fingerprints more disperse. Our experimental results evaluated on LivDet 2017 showed that our proposed CFD-PAD can achieve 2.53% ACE and 93.83% True Detection Rate when the False Detection Rate equals to 1.0% (TDR@FDR=1%) and it outperforms the best single model based methods in terms of ACE (2.53% vs. 4.56%) and TDR@FDR=1%(93.83% vs. 73.32%) significantly, which proves the effectiveness of the proposed method. Although we have achieved a comparable result compared with the state-of-the-art multiple model based method, there still achieves an increase of TDR@FDR=1% from 91.19% to 93.83% by our method. Besides, our model is simpler, lighter and, more efficient and has achieved a 74.76% reduction in time-consuming compared with the state-of-the-art multiple model based method. Code will be publicly available.

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Effective Presentation Attack Detection Driven by Face Related Task

Zhang

Liu

et al. 2022

Lecture Notes in Computer Science

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The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and it is hard to predict the Presentation Attack Instrument (PAI) species that will be used by the attacker. Existing PAD methods are highly dependent on the limited training set and cannot generalize well to unknown PAI species. Unlike this specific PAD task, other face related tasks trained by huge amount of real faces (e.g. face recognition and attribute editing) can be effectively adopted into different application scenarios. Inspired by this, we propose to trade position of PAD and face related work in a face system and apply the free acquired prior knowledge from face related tasks to solve face PAD, so as to improve the generalization ability in detecting PAs. The proposed method, first introduces task specific features from other face related task, then, we design a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such features to adapt to PAD task. Finally, face PAD is achieved by using the hierarchical features from a CNN-based PA detector and the re-mapped features. The experimental results show that the proposed method can achieve significant improvements in the complicated and hybrid datasets, when compared with the state-of-the-art methods. In particular, when training on the datasets OULU-NPU, CASIA-FASD, and Idiap Replay-Attack, we obtain HTER (Half Total Error Rate) of 5.48% for the testing dataset MSU-MFSD, outperforming the baseline by 7.39%. The source code is available at https://wentianzhang-ml.github.io/pad/.

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One-Class Fingerprint Presentation Attack Detection Using Auto-Encoder Network

Cited by 27 publications

References 34 publications

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

Effective Presentation Attack Detection Driven by Face Related Task

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