Search Results for author:
Found 19 papers, 9 papers with code
BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition
Several studies in neuroscience reveal that feature restoration which fills in the occluded information and is called amodal completion is essential for human brains to recognize partially occluded images.
Dual Complementary Dynamic Convolution for Image Recognition
As a powerful engine, vanilla convolution has promoted huge breakthroughs in various computer tasks.
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.
Adversarial Attack across Datasets
In this paper, we define a Generalized Transferable Attack (GTA) problem where the attacker doesn't know this information and is acquired to attack any randomly encountered images that may come from unknown datasets.
Training Meta-Surrogate Model for Transferable Adversarial Attack
In this paper, we tackle this problem from a novel angle -- instead of using the original surrogate models, can we obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easier transferred to other models?
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).
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).
Dual-Cross Central Difference Network for Face Anti-Spoofing
In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively.
Searching for Alignment in Face Recognition
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing.
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems.
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.
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.
