35 citations · 70 across the 13 of their papers we have counts for
13 papers
SteerFace: Debiasing Synthetic Face Generation via Adaptive Residue Perturbation
Yuxi Mi, Qiuyang Yuan, Jianqing Xu +5
The shortage of legally compliant data for face recognition training has sparked growing interest in using synthetic data as an alternative. While recent diffusion-based methods en…
GloTok: Global Perspective Tokenizer for Image Reconstruction and Generation
Xuan Zhao, Zhongyu Zhang, Yuge Huang +6
Existing state-of-the-art image tokenization methods leverage diverse semantic features from pre-trained vision models for additional supervision, to expand the distribution of lat…
ImmerIris: A Large-Scale Dataset and Benchmark for Off-Axis and Unconstrained Iris Recognition in Immersive Applications
Yuxi Mi, Qiuyang Yuan, Zhizhou Zhong +5
Recently, iris recognition is regaining prominence in immersive applications such as extended reality as a means of seamless user identification. This application scenario introduc…
Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion
Yuxi Mi, Zhizhou Zhong, Yuge Huang +7
Identity-preserving face synthesis aims to generate synthetic face images of virtual subjects that can substitute real-world data for training face recognition models. While prior…
UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition
Xiao Lin, Yuge Huang, Jianqing Xu +3
Face recognition (FR) stands as one of the most crucial applications in computer vision. The accuracy of FR models has significantly improved in recent years due to the availabilit…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
SlerpFace: Face Template Protection via Spherical Linear Interpolation
Zhizhou Zhong, Yuxi Mi, Yuge Huang +7
Contemporary face recognition systems use feature templates extracted from face images to identify persons. To enhance privacy, face template protection techniques are widely emplo…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Yuxi Mi, Zhizhou Zhong, Yuge Huang +6
The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper expl…
Privacy-Preserving Face Recognition Using Random Frequency Components
Yuxi Mi, Yuge Huang, Jiazhen Ji +5
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. Thi…
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings
Yuxi Mi, Hongquan Liu, Yewei Xia +3
The emergence of vertical federated learning (VFL) has stimulated concerns about the imperfection in privacy protection, as shared feature embeddings may reveal sensitive informati…
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
Yuxi Mi, Yuge Huang, Jiazhen Ji +4
With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal priva…
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection
Yuxi Mi, Yiheng Sun, Jihong Guan +1
Federated learning has seen increased adoption in recent years in response to the growing regulatory demand for data privacy. However, the opaque local training process of federate…