FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection

• URL: http://arxiv.org/abs/2501.02892v1
• Date: Mon, 06 Jan 2025 10:14:52 GMT
• Title: FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
• Authors: Guray Ozgur, Eduarda Caldeira, Tahar Chettaoui, Fadi Boutros, Raghavendra Ramachandra, Naser Damer,
• Abstract summary: Presentation attack detection (PAD) algorithms need to be generalizable to unseen domains. Foundation models (FM) are pre-trained on extensive datasets, achieving remarkable results when generalizing to unseen domains. We release the implementation of FoundPAD at https://github.com/gurayozgur/FoundPAD.
• Score: 8.605999078286567
• License:
• Abstract: Although face recognition systems have seen a massive performance enhancement in recent years, they are still targeted by threats such as presentation attacks, leading to the need for generalizable presentation attack detection (PAD) algorithms. Current PAD solutions suffer from two main problems: low generalization to unknown cenarios and large training data requirements. Foundation models (FM) are pre-trained on extensive datasets, achieving remarkable results when generalizing to unseen domains and allowing for efficient task-specific adaption even when little training data are available. In this work, we recognize the potential of FMs to address common PAD problems and tackle the PAD task with an adapted FM for the first time. The FM under consideration is adapted with LoRA weights while simultaneously training a classification header. The resultant architecture, FoundPAD, is highly generalizable to unseen domains, achieving competitive results in several settings under different data availability scenarios and even when using synthetic training data. To encourage reproducibility and facilitate further research in PAD, we publicly release the implementation of FoundPAD at https://github.com/gurayozgur/FoundPAD .

Related papers

• Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis [64.46312434121455]
  Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. We propose task-specific FAS data augmentation (FAS-Aug), which increases data diversity by synthesizing data of artifacts. We also propose Spoofing Attack Risk Equalization (SARE) to prevent models from relying on certain types of artifacts and improve the generalization performance.
  arXiv  Detail & Related papers  (2024-09-04T01:45:18Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series [5.487882744996215]
  We propose a novel deep learning-based approach called Class-dependent Automatic Adaptive Policies (CAAP) framework. CAAP framework overcomes the notable class-dependent bias problem while maintaining the overall improvement in time-series data augmentation. We demonstrate that CAAP outperforms representative methods in achieving lower class-dependent bias combined with superior overall performance.
  arXiv  Detail & Related papers  (2024-04-01T03:51:38Z)
• Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics [9.612556145185431]
  Face presentation attack detection (PAD) uses domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. Most DG-based PAD solutions rely on a priori, i.e., known domain labels. This paper proposes to model face PAD as a compound DG task from a causal perspective, linking it to model optimization.
  arXiv  Detail & Related papers  (2023-08-28T13:11:05Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Face Presentation Attack Detection using Taskonomy Feature [26.343512092423985]
  Presentation Attack Detection (PAD) methods are critical to ensure the security of Face Recognition Systems (FRSs) Existing PAD methods are highly dependent on the limited training set and cannot generalize well to unknown PAs. We propose to apply taskonomy (task taxonomy) from other face-related tasks to solve face PAD.
  arXiv  Detail & Related papers  (2021-11-22T08:35:26Z)
• Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation [100.69458267888962]
  Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. Due to legal and privacy issues, training data (real face images and spoof images) are not allowed to be directly shared between different data sources. We propose a Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation framework.
  arXiv  Detail & Related papers  (2021-10-25T02:51:05Z)
• Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection [0.8701566919381223]
  Face Presentation Attack Detection (PAD) has drawn increasing attentions to secure the face recognition systems. We propose an end-to-end learning framework based on Domain Adaptation (DA) to improve PAD generalization capability.
  arXiv  Detail & Related papers  (2021-02-13T05:34:40Z)
• Multi-Domain Adversarial Feature Generalization for Person Re-Identification [52.835955258959785]
  We propose a multi-dataset feature generalization network (MMFA-AAE) It is capable of learning a universal domain-invariant feature representation from multiple labeled datasets and generalizing it to unseen' camera systems. It also surpasses many state-of-the-art supervised methods and unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2020-11-25T08:03:15Z)
• Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning [109.42987031347582]
  Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. We propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net)
  arXiv  Detail & Related papers  (2020-04-04T15:45:14Z)

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.