most citedSDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance

9 citations · 16 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20213 cited

Adaptive Feature Alignment for Adversarial Training

Tao Wang, Ruixin Zhang, Xingyu Chen +6

Recent studies reveal that Convolutional Neural Networks (CNNs) are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. Many advers…

cs.CV20214 cited

Consistent Instance False Positive Improves Fairness in Face Recognition

Xingkun Xu, Yuge Huang, Pengcheng Shen +5

Demographic bias is a significant challenge in practical face recognition systems. Existing methods heavily rely on accurate demographic annotations. However, such annotations are…

cs.CV2021

Federated Face Recognition

Fan Bai, Jiaxiang Wu, Pengcheng Shen +2

Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years. An important issue of face recognition is data privacy, wh…

cs.CV20219 cited

SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance

Fu-Zhao Ou, Xingyu Chen, Ruixin Zhang +6

In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition perf…

cs.CV2020

CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

Yuge Huang, Yuhan Wang, Ying Tai +5

As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recent…