Face Anti-Spoofing: Model Matters, so Does Data

Yang, Xiao; Luo, Wenhan; Bao, Linchao; Gao, Yuan; Gong, Dihong; Zheng, Shuang; Li, Zhifeng; Liu, Wei

doi:10.1109/cvpr.2019.00362

Cited by 187 publications

(132 citation statements)

References 31 publications

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“…Results on SiW. Table 4 compares the performance of our method with four state-of-the-art methods: Auxiliary [6], STASN [8], FAS-TD [13] and CDCN++ [4] on SiW dataset. It can be seen from Table 4 that the proposed NAS-FAS performs the best for all three protocols (0.12%, 0.04% and 1.52% ACER, respectively).…”

Section: Intra-dataset Intra-type Testingmentioning

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.

139

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: Intra-dataset Intra-type Testingmentioning

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.

139

102

View full text Add to dashboard Cite

show abstract

“…Specifically, a CNN-RNN architecture is proposed to utilize depth map information and rPPG (remote Photoplethysmography) signs, which can both exploit spoof patterns across spatial and temporal domains. In [29], an augmented dataset is collected in a specific image synthesis way, which can further improve the robustness of the model. DL-based methods usually have superior classification accuracy when training and testing samples belong to similar scenes.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Towards Face Presentation Attack Detection Based on Residual Color Texture Representation

Qiao

et al. 2021

Security and Communication Networks

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Most existing face authentication systems have limitations when facing the challenge raised by presentation attacks, which probably leads to some dangerous activities when using facial unlocking for smart device, facial access to control system, and face scan payment. Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data preprocessing, and we propose to adopt DW-filter for obtaining residual image, which can effectively improve the detection efficiency. Subsequently, powerful CM texture descriptor is introduced, which performs better than widely used descriptors such as LBP or LPQ. Additionally, representative texture features are extracted from not only RGB space but also more discriminative color spaces such as HSV, YCbCr, and CIE 1976 L∗a∗b (LAB). Meanwhile, the RCTR is fed into the well-designed classifier. Specifically, we compare and analyze the performance of advanced classifiers, among which an ensemble classifier based on a probabilistic voting decision is our optimal choice. Extensive experimental results empirically verify the proposed face presentation attack detector’s superior performance both in the cases of intradataset and interdataset (mismatched training-testing samples) evaluation.

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“…Siddiqui et al [13] used temporal evidence aggregation over face region and scene of video images, which performs well on 2D face attack datasets due to the synthesized multi-features, but fails to judge the 3D realistic masks. In addition, Long Short Term Memory (LSTM) [14] can recurrently learn features to obtain context information, but it suffers from the heavily computational burden. To obtain intrinsic liveness cues, researchers propose a remote photo plethysmography (rPPG) technique [15] to detect the heartbeat signal from face appearance.…”

Section: A Face Anti-spoofingmentioning

confidence: 99%

Data-Fusion-Based Two-Stage Cascade Framework for Multimodality Face Anti-Spoofing

Liu

Wei²,

Lei

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Existing face anti-spoofing models using deep learning for multi-modality data suffer from low generalization in the case of using variety of presentation attacks such as 2D printing and high-precision 3D face masks. One of the main reasons is that the non-linearity of multi-spectral information used to preserve the intrinsic attributes between a real and a fake face are not well extracted. To address this issue, we propose a multi-modility data based two-stage cascade framework for face anti-spoofing. The proposed framework has two advantages. Firstly, we design a two-stage cascade architecture that can selectively fuse lowlevel and high-level features from different modalities to improve feature representation. Secondly, we use multi-modality data to construct a distance-free spectral on RGB and infrared (IR) to augment the non-linearity of data. The presented data fusion strategy is different from popular fusion approaches, since it can strengthen discrimination ability of network models on physical attribute features than identity structure features under certain constraints. In addition, a multi-scale patch based weighted finetuning strategy is designed to learn each specific local face region. Experimental results show that the proposed framework achieves better performance than other state-of-the-art methods on both benchmark datasets and self-established datasets, especially on multi-material masks spoofing.

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Face Anti-Spoofing: Model Matters, so Does Data

Cited by 187 publications

References 31 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

Towards Face Presentation Attack Detection Based on Residual Color Texture Representation

Data-Fusion-Based Two-Stage Cascade Framework for Multimodality Face Anti-Spoofing

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