activity
20202022
most citedPoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

23 citations · 37 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Face Presentation Attack Detection

Zitong Yu, Chenxu Zhao, Zhen Lei

Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment due to its convenience and high accuracy. However, its vul…

cs.CV20211 cited

3D High-Fidelity Mask Face Presentation Attack Detection Challenge

Ajian Liu, Chenxu Zhao, Zitong Yu +8

The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale Hi…

cs.CV202123 cited

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

Qiang Meng, Xiaqing Xu, Xiaobo Wang +6

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.…

cs.CV20213 cited

Searching for Alignment in Face Recognition

Xiaqing Xu, Qiang Meng, Yunxiao Qin +4

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…

cs.CV202010 cited

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Zitong Yu, Yunxiao Qin, Xiaobai Li +4

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, w…

cs.CV2020

Learning Meta Face Recognition in Unseen Domains

Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao +3

Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization. For example, a well-…