Synthetic Face Recognition
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Benchmarks
Most implemented
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition
IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Papers
UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition
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 availability of large-scale human face datasets. Ho…
DiversityFace RecognitionSynthetic Face RecognitionHyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere
Face recognition datasets are often collected by crawling Internet and without individuals' consents, raising ethical and privacy concerns. Generating synthetic datasets for training face recognition models has emerged a…
BenchmarkingDataset GenerationFace RecognitionSynthetic Face RecognitionUnveiling Synthetic Faces: How Synthetic Datasets Can Expose Real Identities
Synthetic data generation is gaining increasing popularity in different computer vision applications. Existing state-of-the-art face recognition models are trained using large-scale face datasets, which are crawled from …
Face RecognitionInference AttackMembership Inference AttackSynthetic Data Generation+1ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
Synthetic face recognition (SFR) aims to generate synthetic face datasets that mimic the distribution of real face data, which allows for training face recognition models in a privacy-preserving manner. Despite the remar…
DiversityFace RecognitionImage GenerationPrivacy Preserving+1Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
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 variability, time and errors produced in …
BenchmarkingFace RecognitionSynthetic Face RecognitionSDFR: Synthetic Data for Face Recognition Competition
Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several…
BenchmarkingFace RecognitionGesture RecognitionSynthetic Face Recognition