Search Results for author:
Found 11 papers, 7 papers with code
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations.
Multi-domain Learning for Updating Face Anti-spoofing Models
In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating.
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization
To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations.
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
Additive process describes spoofing as spoof material introducing extra patterns (e. g., moire pattern), where the live counterpart can be recovered by removing those patterns.
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed "spoof trace", e. g., color distortion, 3D mask edge, Moire pattern, and many others.
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as the genuine user.
Deep Tree Learning for Zero-shot Face Anti-Spoofing
We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA).
Face De-Spoofing: Anti-Spoofing via Noise Modeling
In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification.
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
Face anti-spoofing is the crucial step to prevent face recognition systems from a security breach.
Dense Face Alignment
Face alignment is a classic problem in the computer vision field.
Ranked #2 on
Face Alignment
on AFLW-LFPA
