Face Anti-Spoofing with Human Material Perception

Yu, Zitong; Li, Xiaobai; Niu, Xuesong; Shi, Jingang; Zhao, Guoying

doi:10.1007/978-3-030-58571-6_33

Cited by 135 publications

(72 citation statements)

References 62 publications

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“…Most works [7], [19], [47], [48] treat FAS as a binary classification supervised by simple binary cross-entropy loss. In contrast, pseudo depth labels [6], [9], reflection maps [5], [49], and binary mask label [19] are utilized as auxiliary supervision signals as the pixel-wise guidance is able to learn more detailed information. On the other hand, according to the dynamic discrepancy [13], [14] between live and spoofing faces, several video level methods are presented to exploit the dynamic spatio-temporal [8], [13], [50], [51] or rPPG [6], [52], [53] features for PAD.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years, both traditional [1], [2], [3] and deep learning-based [4], [5], [6], [7], [8], [9], [10] methods have shown effectiveness for presentation attack detection (PAD). On one hand, some classical local descriptors (e.g., local binary pattern (LBP) [11] and histogram of gradient (HOG) [2]) are robust for describing the detailed invariant information (e.g., color texture, moiré pattern and noise artifacts) from spoofing faces.…”

Section: Introductionmentioning

confidence: 99%

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NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing

Wan

Qin

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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181

102

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Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Existing methods heavily rely on the expert-designed networks, which may lead to a sub-optimal solution for FAS task. Here we propose the first FAS method based on neural architecture search (NAS), called NAS-FAS, to discover the well-suited task-aware networks. Unlike previous NAS works mainly focus on developing efficient search strategies in generic object classification, we pay more attention to study the search spaces for FAS task. The challenges of utilizing NAS for FAS are in two folds: the networks searched on 1) a specific acquisition condition might perform poorly in unseen conditions, and 2) particular spoofing attacks might generalize badly for unseen attacks. To overcome these two issues, we develop a novel search space consisting of central difference convolution and pooling operators. Moreover, an efficient static-dynamic representation is exploited for fully mining the FAS-aware spatio-temporal discrepancy. Besides, we propose Domain/Type-aware Meta-NAS, which leverages cross-domain/type knowledge for robust searching. Finally, in order to evaluate the NAS transferability for cross datasets and unknown attack types, we release a large-scale 3D mask dataset, namely CASIA-SURF 3DMask, for supporting the new 'cross-dataset cross-type' testing protocol. Experiments demonstrate that the proposed NAS-FAS achieves state-of-the-art performance on nine FAS benchmark datasets with four testing protocols.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing

Wan

Qin

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

181

102

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show abstract

“…Another interesting approaches include formulating PAD as object detection [63], PAD as material classification [64], using the shape from shading algorithm as a preprocessing [65], and neural architecture search [66]. Recently, Zhang et al created a large-scale face anti-spoofing dataset with annotations [67].…”

Section: Dnnsmentioning

confidence: 99%

Efficient Face Spoofing Detection With Flash

Ebihara

Sakurai

Imaoka

2021

IEEE Trans. Biom. Behav. Identity Sci.

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“…In the past two decades, both traditional [3], [4], [5], [6], [7] and deep learning-based [8], [9], [10], [11], [12], [13], [14] methods have shown their effectiveness for presentation attack detection (PAD). Most traditional algorithms focus on human liveness cues and handcrafted features, which need rich task-aware prior knowledge.…”

mentioning

confidence: 99%

“…However, convolutional neural networks (CNNs) with binary loss might discover arbitrary cues that are able to separate the two classes (e.g., screen bezel), but not the faithful spoofing patterns. Recently, pixel-wise supervision [9], [10], [12], [26], [29], [37] attracts more attention as it provides more fine-grained context-aware supervision signals, which is beneficial for deep models learning intrinsic spoofing cues. As can be seen from Fig.…”

mentioning

confidence: 99%

Revisiting Pixel-Wise Supervision for Face Anti-Spoofing

Shi

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from the presentation attacks (PAs). As more and more realistic PAs with novel types spring up, it is necessary to develop robust algorithms for detecting unknown attacks even in unseen scenarios. However, deep models supervised by traditional binary loss (e.g., '0' for bonafide vs. '1' for PAs) are weak in describing intrinsic and discriminative spoofing patterns. Recently, pixel-wise supervision has been proposed for the FAS task, intending to provide more fine-grained pixel/patchlevel cues. In this paper, we firstly give a comprehensive review and analysis about the existing pixel-wise supervision methods for FAS. Then we propose a novel pyramid supervision, which guides deep models to learn both local details and global semantics from multi-scale spatial context. Extensive experiments are performed on five FAS benchmark datasets to show that, without bells and whistles, the proposed pyramid supervision could not only improve the performance beyond existing pixel-wise supervision frameworks, but also enhance the model's interpretability (i.e., locating the patch-level positions of PAs more reasonably). Furthermore, elaborate studies are conducted for exploring the efficacy of different architecture configurations with two kinds of pixel-wise supervisions (binary mask and depth map supervisions), which provides inspirable insights for future architecture/supervision design.

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Face Anti-Spoofing with Human Material Perception

Cited by 135 publications

References 62 publications

NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing

NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing

Efficient Face Spoofing Detection With Flash

Revisiting Pixel-Wise Supervision for Face Anti-Spoofing

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