Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Liu, Yaojie; Jourabloo, Amin; Liu, Xiaoming

doi:10.1109/cvpr.2018.00048

Cited by 563 publications

(658 citation statements)

References 44 publications

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“…Liu et al [30] presented a novel method for PAD with auxiliary supervision. Instead of training a network end-to-end directly for PAD task, they used CNN-RNN model to estimate the depth with pixel-wise supervision and estimate remote photoplethysmography (rPPG) with sequence-wise supervision.…”

Section: B Cnn Based Approaches For Face Padmentioning

confidence: 99%

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

George

Mostaani

Geissenbuhler

et al. 2020

IEEE Trans.Inform.Forensic Secur.

201

195

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Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation attacks (PA), most of them fail to detect sophisticated attacks such as silicone masks. As the quality of presentation attack instruments improves over time, achieving reliable PA detection with visual spectra alone remains very challenging. We argue that analysis in multiple channels might help to address this issue. In this context, we propose a multi-channel Convolutional Neural Network based approach for presentation attack detection (PAD). We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks. Data from different channels such as color, depth, near-infrared and thermal are available to advance the research in face PAD. The proposed method was compared with feature-based approaches and found to outperform the baselines achieving an ACER of 0.3% on the introduced dataset. The database and the software to reproduce the results are made available publicly.

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Section: B Cnn Based Approaches For Face Padmentioning

confidence: 99%

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

George

Mostaani

Geissenbuhler

et al. 2020

IEEE Trans.Inform.Forensic Secur.

201

195

View full text Add to dashboard Cite

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“…CNN-based methods [15], [16], [42]- [45] have been presented recently in the face PAD community. They treat face PAD as a binary classification problem and achieve remarkable improvements in the intra-testing.…”

Section: B Methodsmentioning

confidence: 99%

“…They treat face PAD as a binary classification problem and achieve remarkable improvements in the intra-testing. Liu et al [15] design a network architecture to leverage two auxiliary information (Depth map and rPPG signal) as supervision. Amin et al [16] introduce a new perspective for solving the face anti-spoofing by inversely decomposing a spoof face into the live face and the spoof noise pattern.…”

Section: B Methodsmentioning

confidence: 99%

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CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

Zhang

Liu

Wan

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

136

109

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Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face antispoofing benchmarks have limited number of subjects (≤ 170) and modalities (≤ 2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face antispoofing in terms of both subjects and modalities. Specifically, it consists of 1, 000 subjects with 21, 000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete/.

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“…The goal is not only to deliver a novel spoofing benchmark by specifying dataset protocols and usage requirements, but also enable the development of more robust anti-spoofing systems with the anticipation of unforeseen wax-based portrait attacks. Although several datasets focus on spoofing detection, most engage in recapturing authentic images or videos in distinct mediums [6,7,8] by varying input sensors, attack types and capture conditions. Only recently have few researchers turned themselves to modeling emerging attack strategies [9,10,11], e.g.…”

Section: Introductionmentioning

confidence: 99%

The Swax Benchmark: Attacking Biometric Systems with Wax Figures

Vareto

Saldanha

Schwartz

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A face spoofing attack occurs when an intruder attempts to impersonate someone who carries a gainful authentication clearance. It is a trending topic due to the increasing demand for biometric authentication on mobile devices, high-security areas, among others. This work introduces a new database named Sense Wax Attack dataset (SWAX), comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations. Experiments performed with baseline methods show that despite the progress in recent years, advanced spoofing methods are still vulnerable to high-quality violation attempts.

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Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Cited by 563 publications

References 44 publications

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

The Swax Benchmark: Attacking Biometric Systems with Wax Figures

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