Single-Side Domain Generalization for Face Anti-Spoofing

Jia, Yunpei; Zhang, Jie; Shan, Shiguang; Chen, Xilin

doi:10.1109/cvpr42600.2020.00851

Cited by 237 publications

(162 citation statements)

References 27 publications

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“…Poor generalizability is more serious in earlier DL-based methods [3]. And, in recent works [30][31][32], such defect is significantly improved.…”

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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“…Poor generalizability is more serious in earlier DL-based methods [3]. And, in recent works [30][31][32], such defect is significantly improved.…”

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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“…Since the above methods did not pay attention to the cross-dataset setting, MADDG [Shao et al, 2019] utilized multiple domain discriminators to learn a generalized feature space. While SSDG [Jia et al, 2020] only aligned the features of real samples from different datasets but not for the features of the fake ones. Moreover, [Chen et al, 2021] proposed D 2 AM to settle a more challenging generalizable scenario in the real world where domain labels are unknown.…”

Section: Face Anti-spoofingmentioning

confidence: 99%

“…To overcome the limitation, some researchers introduce the domain generalization (DG) methods for face presentation attack detection, which utilize several source domains to improve the generalization abilities of the model. Based on it, existing methods [Shao et al, 2019;Jia et al, 2020] attempt to align the features of multiple source data equally for a shared feature space. However, for some samples are little domain-biased and some ones are large domain-biased, training on such samples indiscriminately will corrupt the generalization abilities of the methods.…”

Section: Introductionmentioning

confidence: 99%

Dual Reweighting Domain Generalization for Face Presentation Attack Detection

Liu

Zhang

Yao

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and endeavor to extract a common feature space to improve the generalization. However, due to complex and biased data distribution, directly treating them equally will corrupt the generalization ability. To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization. Concretely, Sample Reweighting Module is first proposed to identify samples with relatively large domain bias, and reduce their impact on the overall optimization. Afterwards, Feature Reweighting Module is introduced to focus on these samples and extract more domain-irrelevant features via a self-distilling mechanism. Combined with the domain discriminator, the iteration of the two modules promotes the extraction of generalized features. Extensive experiments and visualizations are presented to demonstrate the effectiveness and interpretability of our method against the state-of-the-art competitors.

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“…To tackle this problem, zero-shot/fewshot learning [32], [33], on-class representation [34], and anomaly detection [35], [36], [37] approaches are used. Domain adaptation algorithms are also used to tackle the problem [38], [39], [40], [41], [42], [43].…”

Section: Dnnsmentioning

confidence: 99%