# Synthetic Data for Face Recognition: Current State and Future Prospects

arXiv: https://arxiv.org/abs/2305.01021 · HTML: https://mlsanity.com/paper/2305.01021/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 62 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 30 of 37)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs](https://mlsanity.com/paper/2005.09635/) (2020)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning](https://mlsanity.com/paper/2004.11660/) (2020)
- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
- [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022)
- [GAN-Control: Explicitly Controllable GANs](https://mlsanity.com/paper/2101.02477/) (2021)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data](https://mlsanity.com/paper/2206.10520/) (2022)
- [CONFIG: Controllable Neural Face Image Generation](https://mlsanity.com/paper/2005.02671/) (2020)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [Level Playing Field for Million Scale Face Recognition](https://mlsanity.com/paper/1705.00393/) (2017)
- [UMDFaces: An Annotated Face Dataset for Training Deep Networks](https://mlsanity.com/paper/1611.01484/) (2016)
- [This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models](https://mlsanity.com/paper/2101.05084/) (2020)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [Unsupervised Face Recognition using Unlabeled Synthetic Data](https://mlsanity.com/paper/2211.07371/) (2022)

## Built on by (most cited first, 30 of 38)

- [IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models](https://mlsanity.com/paper/2308.04995/) (2023)
- [GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations](https://mlsanity.com/paper/2305.19962/) (2023)
- [Analyzing Bias in Diffusion-based Face Generation Models](https://mlsanity.com/paper/2305.06402/) (2023)
- [SDFR: Synthetic Data for Face Recognition Competition](https://mlsanity.com/paper/2404.04580/) (2024)
- [FRoundation: Are Foundation Models Ready for Face Recognition?](https://mlsanity.com/paper/2410.23831/) (2024)
- [ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation](https://mlsanity.com/paper/2307.05151/) (2023)
- [FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data](https://mlsanity.com/paper/2311.10476/) (2023)
- [Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data](https://mlsanity.com/paper/2412.01383/) (2024)
- [Bias and Diversity in Synthetic-based Face Recognition](https://mlsanity.com/paper/2311.03970/) (2023)
- [ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset](https://mlsanity.com/paper/2404.11236/) (2024)
- [SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data](https://mlsanity.com/paper/2308.14852/) (2023)
- [AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition](https://mlsanity.com/paper/2407.01332/) (2024)
- [VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition](https://mlsanity.com/paper/2412.06235/) (2024)
- [Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics](https://mlsanity.com/paper/2308.14551/) (2023)
- [ID-Booth: Identity-consistent Face Generation with Diffusion Models](https://mlsanity.com/paper/2504.07392/) (2025)
- [SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2311.05336/) (2023)
- [Deep Learning in Palmprint Recognition-A Comprehensive Survey](https://mlsanity.com/paper/2501.01166/) (2025)
- [If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces](https://mlsanity.com/paper/2404.03537/) (2024)
- [A Survey on Drowsiness Detection -- Modern Applications and Methods](https://mlsanity.com/paper/2408.12990/) (2024)
- [FROQ: Observing Face Recognition Models for Efficient Quality Assessment](https://mlsanity.com/paper/2509.17689/) (2025)
- [The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition](https://mlsanity.com/paper/2409.02867/) (2024)
- [SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples](https://mlsanity.com/paper/2409.05595/) (2024)
- [NegFaceDiff: The Power of Negative Context in Identity-Conditioned Diffusion for Synthetic Face Generation](https://mlsanity.com/paper/2508.09661/) (2025)
- [CLIP4Sketch: Enhancing Sketch to Mugshot Matching through Dataset Augmentation using Diffusion Models](https://mlsanity.com/paper/2408.01233/) (2024)
- [IDperturb: Enhancing Variation in Synthetic Face Generation via Angular Perturbation](https://mlsanity.com/paper/2602.18831/) (2026)
- [Face Recognition Using Synthetic Face Data](https://mlsanity.com/paper/2305.10079/) (2023)
- [How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition](https://mlsanity.com/paper/2408.17399/) (2024)
- [PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models](https://mlsanity.com/paper/2605.22311/) (2026)
- [Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks](https://mlsanity.com/paper/2311.11882/) (2023)
- [DiffProb: Data Pruning for Face Recognition](https://mlsanity.com/paper/2505.15272/) (2025)

## Abstract (arXiv)

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, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition.