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20172023
most citedCross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

304 citations · 1k across the 30 of their papers we have counts for

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43 papers · 1 filter

cs.CV2023★ 1 cited

Survey on Deep Face Restoration: From Non-blind to Blind and Beyond

Wenjie Li, Mei Wang, Kai Zhang +6

Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep…

cs.CV2023★ 124 cited

SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation

Lixiong Qin, Mei Wang, Chao Deng +4

In recent years, vision transformers have been introduced into face recognition and analysis and have achieved performance breakthroughs. However, most previous methods generally t…

cs.CV2023★ 4 cited

Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation

Xuannan Liu, Yaoyao Zhong, Yuhang Zhang +2

Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples. Compared to…

cs.CV2023

Adaptive Face Recognition Using Adversarial Information Network

Mei Wang, Weihong Deng

In many real-world applications, face recognition models often degenerate when training data (referred to as source domain) are different from testing data (referred to as target d…

cs.CV2023★ 1 cited

Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention

Linzhi Huang, Mei Wang, Jiahao Liang +5

Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work f…

cs.CV2023★ 3 cited

Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective

Yuhang Zhang, Weihong Deng, Liang Zheng

Out-of-distribution (OOD) detection methods assume that they have test ground truths, i.e., whether individual test samples are in-distribution (IND) or OOD. However, in the real w…