SDFR: Synthetic Data for Face Recognition Competition

• URL: http://arxiv.org/abs/2404.04580v2
• Date: Tue, 9 Apr 2024 06:56:02 GMT
• Title: SDFR: Synthetic Data for Face Recognition Competition
• Authors: Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George, Alexander Unnervik, Sébastien Marcel, Nicolò Di Domenico, Guido Borghi, Davide Maltoni, Fadi Boutros, Julia Vogel, Naser Damer, Ángela Sánchez-Pérez, EnriqueMas-Candela, Jorge Calvo-Zaragoza, Bernardo Biesseck, Pedro Vidal, Roger Granada, David Menotti, Ivan DeAndres-Tame, Simone Maurizio La Cava, Sara Concas, Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez, Gianpaolo Perelli, Giulia Orrù, Gian Luca Marcialis, Julian Fierrez,
• Abstract summary: Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
• Score: 51.9134406629509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed.

Related papers

• Unveiling Synthetic Faces: How Synthetic Datasets Can Expose Real Identities [22.8742248559748]
  We show that in 6 state-of-the-art synthetic face recognition datasets, several samples from the original real dataset are leaked. This paper is the first work which shows the leakage from training data of generator models into the generated synthetic face recognition datasets.
  arXiv  Detail & Related papers  (2024-10-31T15:17:14Z)
• Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data [104.45155847778584]
  This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations.
  arXiv  Detail & Related papers  (2024-04-16T08:15:10Z)
• If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces [16.977459035497162]
  Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this paper, we examine whether and how synthetic face data can be used to train effective face recognition models.
  arXiv  Detail & Related papers  (2024-04-04T15:45:25Z)
• FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data [82.5767720132393]
  This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology.
  arXiv  Detail & Related papers  (2023-11-17T12:15:40Z)
• Learning Human Action Recognition Representations Without Real Humans [66.61527869763819]
  We present a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model. We then evaluate the transferability of the representation learned on this data to a diverse set of downstream action recognition benchmarks. Our approach outperforms previous baselines by up to 5%.
  arXiv  Detail & Related papers  (2023-11-10T18:38:14Z)
• SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data [8.026313049094146]
  State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. We propose a new framework to train lightweight face recognition models by distilling the knowledge of a pretrained teacher face recognition model using synthetic data. We use synthetic face images without identity labels, mitigating the problems in the intra-class variation generation of synthetic datasets.
  arXiv  Detail & Related papers  (2023-08-28T19:15:27Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• SynFace: Face Recognition with Synthetic Data [83.15838126703719]
  We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap. We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
  arXiv  Detail & Related papers  (2021-08-18T03:41:54Z)
• On the use of automatically generated synthetic image datasets for benchmarking face recognition [2.0196229393131726]
  Recent advances in Generative Adversarial Networks (GANs) provide a pathway to replace real datasets by synthetic datasets. Recent advances in Generative Adversarial Networks (GANs) to synthesize realistic face images provide a pathway to replace real datasets by synthetic datasets. benchmarking results on the synthetic dataset are a good substitution, often providing error rates and system ranking similar to the benchmarking on the real dataset.
  arXiv  Detail & Related papers  (2021-06-08T09:54:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.