Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

By Rui Shao, Xiangyuan Lan, Jiawei Li, Pong Chi Yuen. 2019

416 citations recorded by the source corpus.

Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Detection and Continual Learning of Novel Face Presentation Attacks (2021, background): “[32] proposed to learn an embedding space that is discriminative across several source domains to improve generalizability of the PAD model on novel PAs in new domains.”
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations (2020, background): “[31] adopted transfer learning to further improve performance.”
Face Anti-Spoofing with Human Material Perception (2020, background): “In order to learn generalized features for unseen attacks and environment, few-shot learning [8], zero-shot learning [8,29] and domain generalization [30,31,32] are introduced for FAS task.”
Domain Generalization: A Survey (2021, methodology): “Conventional face anti-spoofing methods do not take into account distribution shift, making them vulnerable to unseen attack types [117].”
Towards Out-Of-Distribution Generalization: A Survey (2021, neutral): “…( Section 3.2 ) [34], [35], [36](Section 3.2) SupervisedLearningModels ( Section 4 ) ( Section 4.1 ) [7], [8], [9], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49] [37], [39], [50], [51], [51], [52], [53], [53], [54], [54], [55], [56], [57], [58], [59], [60]…”
A Fourier-based Framework for Domain Generalization (2021, methodology): “To tackle the DG problem, many existing methods utilize adversarial training [24, 23, 38], meta learning [21, 1, 25, 4], self-supervised learning [2] or domain augmentation [44, 37, 50, 51] techniques and have shown promising results.”
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification (2020, background): “Some work [22,26,41] has also investigated using multi-scale or different convolutional channels to capture the pixel-level/small-region-level attentions [35,36].”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, background): “Face presentation attacks are also studied in [255].”
Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning (2020, methodology): “We can observe that conventional PAD methods usually perform worse than the domain generalized methods like Auxiliary-supervision [30], MMD-AAE [26], MADDG [38].”
Domain Generalization Using a Mixture of Multiple Latent Domains (2019, background): “To deal with domain shift (Torralba and Efros 2011), domain adaptation and domain generalization have been studied.”
Single-Side Domain Generalization for Face Anti-Spoofing (2020, methodology): “21 MADDG [33] 41.”