Unsupervised Compound Domain Adaptation for Face Anti-Spoofing

• URL: http://arxiv.org/abs/2105.08463v1
• Date: Tue, 18 May 2021 12:08:07 GMT
• Title: Unsupervised Compound Domain Adaptation for Face Anti-Spoofing
• Authors: Ankush Panwar, Pratyush Singh, Suman Saha, Danda Pani Paudel and Luc Van Gool
• Abstract summary: We address the problem of face anti-spoofing which aims to make the face verification systems robust in the real world settings. We propose a memory augmentation method for adapting the source model to the target domain in a domain aware manner. The proposed method successfully adapts to the compound target domain consisting multiple new spoof types.
• Score: 74.6122128643823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of face anti-spoofing which aims to make the face verification systems robust in the real world settings. The context of detecting live vs. spoofed face images may differ significantly in the target domain, when compared to that of labeled source domain where the model is trained. Such difference may be caused due to new and unknown spoof types, illumination conditions, scene backgrounds, among many others. These varieties of differences make the target a compound domain, thus calling for the problem of the unsupervised compound domain adaptation. We demonstrate the effectiveness of the compound domain assumption for the task of face anti-spoofing, for the first time in this work. To this end, we propose a memory augmentation method for adapting the source model to the target domain in a domain aware manner. The adaptation process is further improved by using the curriculum learning and the domain agnostic source network training approaches. The proposed method successfully adapts to the compound target domain consisting multiple new spoof types. Our experiments on multiple benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art.

Related papers

• Cyclically Disentangled Feature Translation for Face Anti-spoofing [61.70377630461084]
  We propose a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN) CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels.
  arXiv  Detail & Related papers  (2022-12-07T14:12:34Z)
• Generative Domain Adaptation for Face Anti-Spoofing [38.12738183385737]
  Face anti-spoofing approaches based on unsupervised domain adaption (UDA) have drawn growing attention due to promising performances for target scenarios. Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features. We propose a novel perspective of UDA FAS that directly fits the target data to the models, stylizes the target data to the source-domain style via image translation, and further feeds the stylized data into the well-trained source model for classification.
  arXiv  Detail & Related papers  (2022-07-20T16:24:57Z)
• One-Class Knowledge Distillation for Face Presentation Attack Detection [53.30584138746973]
  This paper introduces a teacher-student framework to improve the cross-domain performance of face PAD with one-class domain adaptation. Student networks are trained to mimic the teacher network and learn similar representations for genuine face samples of the target domain. In the test phase, the similarity score between the representations of the teacher and student networks is used to distinguish attacks from genuine ones.
  arXiv  Detail & Related papers  (2022-05-08T06:20:59Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks [33.84004077585957]
  We propose an end-to-end novel semantic consistent generative adversarial network (SCGAN) This network can achieve source to target domain matching by capturing semantic information at the feature level. We demonstrate the robustness of our proposed method which exceeds the state-of-the-art performance in unsupervised domain adaptation settings.
  arXiv  Detail & Related papers  (2021-04-28T12:23:30Z)
• Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation [169.82760468633236]
  We propose to build the pixel-level cycle association between source and target pixel pairs. Our method can be trained end-to-end in one stage and introduces no additional parameters.
  arXiv  Detail & Related papers  (2020-10-31T00:11:36Z)
• Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation [13.163271874039191]
  We present a novel approach to perform the unsupervised domain adaptation for object detection through forward-backward cyclic (FBC) training. Recent adversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. We propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing.
  arXiv  Detail & Related papers  (2020-02-03T06:24:58Z)
• CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency [119.45667331836583]
  Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another. We present a novel pixel-wise adversarial domain adaptation algorithm.
  arXiv  Detail & Related papers  (2020-01-09T19:00:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.