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Found 13 papers, 7 papers with code
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.
Consistency Regularization for Deep Face Anti-Spoofing
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
Meta-Teacher For Face Anti-Spoofing
The meta-teacher is trained in a bi-level optimization manner to learn the ability to supervise the PA detectors learning rich spoofing cues.
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e. g., in cases of surveillance and photo-tagging).
Layer-Wise Adaptive Updating for Few-Shot Image Classification
LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images.
Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks.
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Ranked #4 on
Face Anti-Spoofing
on OULU-NPU
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Therefore, we define face anti-spoofing as a zero- and few-shot learning problem.
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning
In this paper, inspired by the human cognition process which utilizes both prior-knowledge and vision attention in learning new knowledge, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and prior-knowledge for meta-learning.
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.
Representation based and Attention augmented Meta learning
The method RAML aims to give the Meta learner the ability of leveraging the past learned knowledge to reduce the dimension of the original input data by expressing it into high representations, and help the Meta learner to perform well.
Exploiting temporal and depth information for multi-frame face anti-spoofing
Face anti-spoofing is significant to the security of face recognition systems.
