70 citations · 157 across the 11 of their papers we have counts for
12 papers
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
Yiyou Sun, Yaojie Liu, Xiaoming Liu +2
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations. Most prior works regard do…
Multi-domain Learning for Updating Face Anti-spoofing Models
Xiao Guo, Yaojie Liu, Anil Jain +1
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 dom…
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox
Yunyun Wang, Yao Liu, Songcan Chen
Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation. Its main challenge lies in how…
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
Hsin-Ping Huang, Deqing Sun, Yaojie Liu +5
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…
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
Yaojie Liu, Xiaoming Liu
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.…
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
Yaojie Liu, Joel Stehouwer, Xiaoming Liu
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
Joel Stehouwer, Amin Jourabloo, Yaojie Liu +1
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…
Deep Tree Learning for Zero-shot Face Anti-Spoofing
Yaojie Liu, Joel Stehouwer, Amin Jourabloo +1
Face anti-spoofing is designed to keep face recognition systems from recognizing fake faces as the genuine users. While advanced face anti-spoofing methods are developed, new types…
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
Yao Liu, Ying Tai, Jilin Li +6
In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-wor…
Face De-Spoofing: Anti-Spoofing via Noise Modeling
Amin Jourabloo, Yaojie Liu, Xiaoming Liu
Many prior face anti-spoofing works develop discriminative models for recognizing the subtle differences between live and spoof faces. Those approaches often regard the image as an…
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
Yaojie Liu, Amin Jourabloo, Xiaoming Liu
Face anti-spoofing is the crucial step to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classi…
Dense Face Alignment
Yaojie Liu, Amin Jourabloo, William Ren +1
Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial land…