activity
20182023
most citedRetinaFace: Single-stage Dense Face Localisation in the Wild

423 citations · 451 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2023★ 1 cited

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

Dong Wang, Jia Guo, Qiqi Shao +9

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of exi…

cs.CV2022★ 2 cited

3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views

Evangelos Ververas, Polydefkis Gkagkos, Jiankang Deng +3

Developing gaze estimation models that generalize well to unseen domains and in-the-wild conditions remains a challenge with no known best solution. This is mostly due to the diffi…

cs.CV2022★ 1 cited

Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression

Jia Guo, Jinke Yu, Alexandros Lattas +1

Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruc…

cs.CV2019★ 423 cited

RetinaFace: Single-stage Dense Face Localisation in the Wild

Jiankang Deng, Jia Guo, Yuxiang Zhou +3

Though tremendous strides have been made in uncontrolled face detection, accurate and efficient face localisation in the wild remains an open challenge. This paper presents a robus…

cs.CV2018★ 24 cited

Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment

Jia Guo, Jiankang Deng, Niannan Xue +1

Facial landmark localisation in images captured in-the-wild is an important and challenging problem. The current state-of-the-art revolves around certain kinds of Deep Convolutiona…

cs.CV2018

Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate

Sheng Chen, Jia Guo, Yang Liu +2

In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 no…