A Database for Face Presentation Attack Using Wax Figure Faces

Jia, Shan; Hu, Chuanbo; Guo, Guodong; Wang, Chao

doi:10.1007/978-3-030-30754-7_5

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…For example, 3D facial biometrics was introduced to overcome the limitations of 2D face scanning biometrics, as mere images were able to spoof them. But still, those systems were still vulnerable to 3D face model based counterfeiting attacks [66]. As a result, researchers from this domain have not only been attempting to improve the existing systems but also are constantly looking for more reliable alternatives.…”

Section: Discussionmentioning

confidence: 99%

EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication

Ibtehaz,

Chowdhury,

Khandakar

et al. 2021

Preprint

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In recent years, physiological signal based authentication has shown great promises, for its inherent robustness against forgery. Electrocardiogram (ECG) signal, being the most widely studied biosignal, has also received the highest level of attention in this regard. It has been proven with numerous studies that by analyzing ECG signals from different persons, it is possible to identify them, with acceptable accuracy. In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system. Moreover, we hypothesize and demonstrate that Siamese architectures can be used over typical distance metrics for improved performance. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using less number of beats. EDITH performs competitively using just a single heartbeat (96 ∼ 99.75% accuracy) and can be further enhanced by fusing multiple beats (100% accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29 %. A limited case study of EDITH with real-world experimental data also suggests its potential as a practical authentication system.

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Section: Discussionmentioning

confidence: 99%

EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication

Ibtehaz,

Chowdhury,

Khandakar

et al. 2021

Preprint

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“…To further demonstrate the effectiveness of the second contribution of this article i.e., the development of a robust algorithm across different attacks, additional experiments are performed on the existing benchmark databases in the VIS spectrum. The performance of the proposed algorithm is evaluated on the CASIA-Face Anti-Spoofing Database (FASD) (Zhang et al, 2012), Replay-Attack (Chingovska et al, 2012), MSU-MFSD (Wen et al, 2015), 3D Mask Attack Database (3DMAD) (Erdogmus and Marcel, 2013), MSU USSA database (Patel et al, 2016), WMCA , SiW-M (Liu et al, 2019b), Silicone Mask Attack Database (SMAD) (Manjani et al, 2017), and 3D wax figure face database (WFFD) (Jia et al, 2019). These databases cover a wide spectrum of attacks such as print, photo, a replay of video, 3D hard resin masks, and the most challenging silicone mask.…”

Section: Experiments On Existing Vis Spectrum Databasesmentioning

confidence: 99%

“…Recently, a new modality of 3D attack is highlighted where wax figure faces are used as a possible adversary on face recognition systems (Jia et al, 2019). The authors have shown that state-of-the-art face recognition algorithms such as OpenFace (Amos et al, 2016) and Face++ 5 are vulnerable to wax figure faces.…”

Section: Experiments On Existing Vis Spectrum Databasesmentioning

confidence: 99%

Boosting Face Presentation Attack Detection in Multi-Spectral Videos Through Score Fusion of Wavelet Partition Images

Singh¹,

Vatsa²,

Noore³

2022

Front. Big Data

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Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm. With this motivation, as the first contribution, the article presents a unified PAD algorithm for different kinds of attacks such as printed photos, a replay of video, 3D masks, silicone masks, and wax faces. The proposed algorithm utilizes a combination of wavelet decomposed raw input images from sensor and face region data to detect whether the input image is bonafide or attacked. The second contribution of the article is the collection of a large presentation attack database in the NIR spectrum, containing images from individuals of two ethnicities. The database contains 500 print attack videos which comprise approximately 1,00,000 frames collectively in the NIR spectrum. Extensive evaluation of the algorithm on NIR images as well as visible spectrum images obtained from existing benchmark databases shows that the proposed algorithm yields state-of-the-art results and surpassed several complex and state-of-the-art algorithms. For instance, on benchmark datasets, namely CASIA-FASD, Replay-Attack, and MSU-MFSD, the proposed algorithm achieves a maximum error of 0.92% which is significantly lower than state-of-the-art attack detection algorithms.

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“…Most 3D PAD datasets are based on wearable hard-resin or silicone masks. Wax figure face databases were reported in [ 34 , 35 ] for super-realistic 3D presentation attack research. For the work presented in this paper, a database has been locally collected as none of the public databases include the type of challenge–response, artefacts and device formats, which are investigated in this paper.…”

Section: State Of the Artmentioning

confidence: 99%

Directed Gaze Trajectories for Biometric Presentation Attack Detection

Ali

Hoque

Deravi

2021

Sensors

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Presentation attack artefacts can be used to subvert the operation of biometric systems by being presented to the sensors of such systems. In this work, we propose the use of visual stimuli with randomised trajectories to stimulate eye movements for the detection of such spoofing attacks. The presentation of a moving visual challenge is used to ensure that some pupillary motion is stimulated and then captured with a camera. Various types of challenge trajectories are explored on different planar geometries representing prospective devices where the challenge could be presented to users. To evaluate the system, photo, 2D mask and 3D mask attack artefacts were used and pupillary movement data were captured from 80 volunteers performing genuine and spoofing attempts. The results support the potential of the proposed features for the detection of biometric presentation attacks.

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A Database for Face Presentation Attack Using Wax Figure Faces

Cited by 12 publications

References 35 publications

EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication

EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication

Boosting Face Presentation Attack Detection in Multi-Spectral Videos Through Score Fusion of Wavelet Partition Images

Directed Gaze Trajectories for Biometric Presentation Attack Detection

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