Generalized Face Liveness Detection via De-fake Face Generator
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an Anomalous cue Guided Face Anti-spoofing (AG-FAS) method. It leverages real face data to improve generalization and detect unseen presentation attacks effectively.
Area Of Science
- Computer Science
- Artificial Intelligence
- Biometrics
Background
- Existing Face Anti-spoofing (FAS) methods struggle with generalization to new domains due to limited and non-diverse datasets.
- Large datasets of real face images, readily available from face recognition advancements, have been underutilized in FAS research.
Purpose Of The Study
- To develop a novel Face Anti-spoofing (FAS) method that enhances model generalization by utilizing extensive real face image data.
- To introduce an Anomalous cue Guided FAS (AG-FAS) approach incorporating a De-fake Face Generator (DFG) and an Off-real Attention Network (OA-Net).
Main Methods
- A De-fake Face Generator (DFG) is trained on a large-scale dataset of real faces to learn authentic facial characteristics.
- The DFG generates a "real" version of any input face, and the difference between the input and generated face serves as an anomalous cue.
- An Off-real Attention Network (OA-Net) is proposed to focus on spoof regions using these anomalous cues for improved fake feature learning.
Main Results
- The proposed AG-FAS method achieves state-of-the-art performance on cross-domain evaluations across nine public datasets.
- The method demonstrates effectiveness against unseen scenarios and unknown presentation attack types.
- Theoretical analysis confirms the efficacy of the anomalous cues in enhancing FAS performance.
Conclusions
- The AG-FAS method effectively addresses the generalization challenge in Face Anti-spoofing by leveraging abundant real face data.
- The proposed DFG and OA-Net components provide a robust framework for detecting sophisticated spoofing attacks.
- This approach offers a promising direction for developing more resilient and generalizable FAS systems.
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