Papers Synthetic Face Recognition
“Synthetic Face Recognition” 태그가 달린 논문 12편 · 필터 해제
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 RecognitionBias and Diversity in Synthetic-based Face Recognition
Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. Howe…
AttributeDiversityFace RecognitionSynthetic Face RecognitionSynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets. Meanw…
Face RecognitionKnowledge DistillationLightweight Face RecognitionSynthetic Face RecognitionIDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade. However, legal and ethical concerns led to the recent retracti…
Face RecognitionSynthetic Face RecognitionIdentity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and dist…
Face RecognitionGenerative Adversarial NetworkSynthetic Face RecognitionDCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Generating synthetic datasets for training face recognition models is challenging because dataset generation entails more than creating high fidelity images. It involves generating multiple images of same subjects under …
Dataset GenerationFace GenerationFace RecognitionSynthetic Face RecognitionDigiFace-1M: 1 Million Digital Face Images for Face Recognition
State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale datasets that contain millions of real human face…
AttributeFace RecognitionSynthetic Data GenerationSynthetic Face Recognition