Synthetic data for face recognition: Current state and future prospects
Over the past years, deep learning capabilities and the availability of large-scale training
datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However …
datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However …
Dcface: 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 …
dataset generation entails more than creating high fidelity images. It involves generating …
Continual diffusion: Continual customization of text-to-image diffusion with c-lora
Recent works demonstrate a remarkable ability to customize text-to-image diffusion models
while only providing a few example images. What happens if you try to customize such …
while only providing a few example images. What happens if you try to customize such …
Idiff-face: Synthetic-based face recognition through fizzy identity-conditioned diffusion model
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 …
advances made in face recognition research over the past decade. However, legal and …
Gandiffface: Controllable generation of synthetic datasets for face recognition with realistic variations
Face recognition systems have significantly advanced in recent years, driven by the
availability of large-scale datasets. However, several issues have recently came up …
availability of large-scale datasets. However, several issues have recently came up …
[HTML][HTML] FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems
This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where
researchers can easily benchmark their systems against the state of the art in an open …
researchers can easily benchmark their systems against the state of the art in an open …
Learning to generate image embeddings with user-level differential privacy
Small on-device models have been successfully trained with user-level differential privacy
(DP) for next word prediction and image classification tasks in the past. However, existing …
(DP) for next word prediction and image classification tasks in the past. However, existing …
Identity-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 …
the internet without proper user consent. Due to the increasing focus on privacy in the social …
Invariant feature regularization for fair face recognition
Fair face recognition is all about learning invariant feature that generalizes to unseen faces
in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced …
in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced …
ILSH: The imperial light-stage head dataset for human head view synthesis
This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-
captured human head dataset designed to support view synthesis academic challenges for …
captured human head dataset designed to support view synthesis academic challenges for …