Synthetic Face Recognition

7 papers with code • 5 benchmarks • 3 datasets

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Most implemented papers

Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition

fdbtrs/synthetic-face-recognition 30 Apr 2023

We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation.

SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

otroshi/synthdistill 28 Aug 2023

While generating synthetic datasets for training face recognition models is an alternative option, it is challenging to generate synthetic data with sufficient intra-class variations.

Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data

otroshi/synthdistill 16 Apr 2024

Synthetic data is gaining increasing relevance for training machine learning models.

DigiFace-1M: 1 Million Digital Face Images for Face Recognition

microsoft/digiface1m 5 Oct 2022

Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet.

DCFace: Synthetic Face Generation with Dual Condition Diffusion Model

mk-minchul/dcface CVPR 2023

Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.

IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models

fdbtrs/IDiff-Face 9 Aug 2023

The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade.

UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition

Tencent/TFace 27 Feb 2025

While existing synthetic-based face recognition methods have made significant progress in generating identity-preserving images, they are severely plagued by context overfitting, resulting in a lack of intra-class diversity of generated images and poor face recognition performance.