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VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition

T0 review · 2 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read VariFace generates synthetic face datasets that, at scale, train face verification models to outperform models trained on real web-scraped data.

desk verdict A serious, well-engineered synthetic-face pipeline with a headline result that needs one missing control and honest error bars before the 'beats real data' claim is fully convincing. read the letter →

arxiv 2412.06235 v2 pith:ZJ4F265I submitted 2024-12-09 cs.CV cs.LG

Michael Yeung , Toya Teramoto , Songtao Wu , Tatsuo Fujiwara , Kenji Suzuki , Tamaki Kojima This is my paper · ORCID
classification cs.CVcs.LG
keywords syntheticfacerecognitiondiffusionmodelsdatasetgenerationfairnessverificationVendiscoredivergencedemographiclabelrefinement
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

VariFace is a two-stage diffusion pipeline that generates synthetic face datasets for training face recognition models, and the paper's central claim is that such synthetic-only data can now match and, at larger scale, beat models trained on real web-scraped faces. At fixed dataset size, VariFace raises the synthetic-data state of the art from 0.9200 to 0.9405 average verification accuracy and narrows the gap to real data to -0.0065. Scaled to 6 million images, it reports an average accuracy of 0.9567 across LFW, CFP-FP, CPLFW, AgeDB, and CALFW, a Real Gap of +0.0097 over CASIA-WebFace, plus 0.9366 on the RFW benchmark, the first reported synthetic-only results exceeding the real training set. The fairness claim matters because the pipeline deliberately balances race and gender in the generated identities and improves verification on African, Asian, and Indian subgroups relative to real data. The practical stakes are that face recognition training may no longer require large privacy-sensitive scraped datasets.

What carries the argument

The load-bearing object is the VariFace two-stage conditional diffusion pipeline built on an Hourglass Diffusion Transformer architecture, with three named control mechanisms. Face Recognition Consistency turns the well-structured geometry of a pretrained face-embedding space into a denoising step for demographic labels: each image's race and gender label is replaced by the majority label among its top-K cosine-similarity neighbours, correcting CLIP's weak spots on minority groups. Face Vendi Score Guidance uses the Vendi score, the exponential Shannon entropy of the normalized eigenvalues of a cosine-similarity kernel matrix, as a differentiable loss during stage-1 sampling, pushing each batch of generated identities to be more diverse. Divergence Score Conditioning defines, for image j of identity i, the cosine similarity between that image's face embedding and the identity's mean embedding, and uses this scalar as a condition so stage-2 generation can dial a range of intraclass variation while a cosine-similarity filter removes identity-loss cases. The pipeline also claims efficiency: 500K images in about 12 hours on one A100, because the diversity guidance is applied only to the small number of identities and no auxiliary attribute classifiers are needed.

What would settle it

Generate two synthetic datasets with the same pipeline but two different pretrained face-recognition teachers, one trained on all demographic groups and one trained on a single group, then train fresh students on each and evaluate on the race-balanced RFW benchmark; if the two students perform the same, the teacher is not the source of the gains, and if the balanced-teacher student loses its edge, the central claim depends on the teacher rather than on the synthetic data itself.

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Extended reading notes

Core claim

The paper claims that a synthetic dataset constructed by VariFace is sufficient, by itself, to train a face recognition model that outperforms one trained on the real CASIA-WebFace dataset. The two-stage design first generates a demographically balanced set of synthetic identities, then generates many images per identity while controlling the identity-preservation and diversity trade-off. Three mechanisms carry the result: Face Recognition Consistency refines CLIP-derived race and gender labels using the structure of a pretrained face-embedding space; Face Vendi Score Guidance maximizes a dataset-level diversity score during stage-1 sampling to spread identities apart; and Divergence Score Conditioning labels each image by its cosine similarity to its identity's mean embedding, letting the second stage vary pose, lighting, age, and other unlabeled attributes without destroying identity. The best trained model achieves an average verification accuracy of 0.9567 on the standard benchmark and 0.9366 on RFW, corresponding to Real Gaps of +0.0097 and +0.0380 over CASIA-WebFace.

Load-bearing premise

The load-bearing premise is that the pretrained face-recognition model used to measure identity similarity, divergence scores, and diversity guidance represents faces of all demographic groups equally well; if that model is biased or already saturated, the synthetic data may only be copying the teacher's blind spots rather than teaching general face recognition.

Editorial extensions

If this is right

  • If the result holds, face recognition models can be trained entirely on synthetic identities, removing the legal and ethical reliance on web-scraped face images.
  • Scaling synthetic datasets adds accuracy: VariFace crosses the real-data baseline at 1.2M images and improves further at 3M and 6M, so dataset size remains a lever when data is generated rather than collected.
  • Deliberate demographic balancing improves not only fairness metrics but average verification: minority-race subgroups African, Asian, and Indian all exceed real-data accuracy on RFW at 6M.
  • Even under the same-size constraint, VariFace's synthetic data is close to real-data performance (Real Gap -0.0065) and clearly ahead of earlier synthetic methods, which stay at Real Gaps between -0.0270 and -0.2681.
  • Generation time is practical: about 12 hours for 500K images, compared with 20 hours for DCFace and 36 hours for Vec2Face, making iterative dataset design feasible.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the pipeline generalizes, the same two-stage recipe of diversity guidance on identities plus a scalar divergence condition on instances could transfer to other recognition domains, such as vehicle or medical imaging, where per-identity variation matters and attribute labels are scarce.
  • Editorial inference: the fairness gains are partly inherited rather than invented, because labels and embeddings come from a pretrained face-recognition model trained on real data, so VariFace's balance is only as good as that teacher's geometry; swapping the teacher for a 3D-rendered or differentially private model would reveal how much of the margin is intrinsic to the pipeline.
  • Editorial inference: the paper's own privacy analysis, reporting maximum cosine similarities to CASIA-WebFace mostly below 0.3, suggests generated identities are not copies of real ones, but a stricter membership-inference attack on the synthetic set would be a stronger test of that privacy claim.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper proposes VariFace, a two-stage diffusion-based pipeline for generating synthetic face datasets for face recognition (FR) training. Stage 1 generates a demographically balanced set of synthetic identities using CLIP-based demographic labels refined by Face Recognition Consistency (FRC), with Face Vendi Score Guidance (FVSG) to promote interclass diversity. Stage 2 generates intraclass variations conditioned on identity embeddings, age, and a Divergence Score (DS) that controls the identity-preservation/diversity trade-off. Experiments compare FR models (IResNet-50, ArcFace) trained on VariFace against models trained on real CASIA-WebFace and on prior synthetic datasets. At matched dataset size (0.5M images), VariFace achieves 0.9405 average accuracy on five standard benchmarks (Real Gap -0.0065) and 0.8978 on RFW (Real Gap -0.0008). When scaled to 6M images, VariFace reaches 0.9567 average on the standard benchmarks (Real Gap +0.0097) and 0.9366 on RFW (Real Gap +0.0380), which the paper claims is the first time a synthetic dataset outperforms a real dataset.

Significance. If the claims hold, this is a notable result: it would demonstrate that synthetic data can replace web-scraped data for training face recognition models, with better demographic balance (RFW minority-race gains) and controllable diversity. The paper's strengths include matched-training reproductions of prior synthetic datasets (Table S7), ablations of the conditioning signals and filtering steps (Tables 6-8, S1-S2), and a privacy analysis comparing synthetic identities with real ones (Fig. S6). However, the headline 'outperform real data' result is sensitive to two unresolved issues: the pervasive use of a single pretrained FR teacher throughout the pipeline, and the absence of error bars for the small performance margins.

major comments (2)
  1. [Sec. 3.1-3.2, Algorithm 1; Tables 3-4] The pipeline uses one pretrained IResNet-100 teacher trained on CASIA-WebFace for identity embeddings (Eq. 4), divergence scores (Eq. 5), FRC label refinement, FVSG gradients (Algorithm 1), and stage-2 ID-preservation filtering. The student IResNet-50 trained on the resulting dataset may therefore be distilling the teacher's embedding geometry rather than benefiting from an intrinsic property of synthetic data. Since the teacher is trained on the same real distribution as the baseline, the claim that VariFace 'for the first time, outperforms the real dataset' is ambiguous. Please include a control that separates these explanations, such as (a) training a student on real CASIA-WebFace with teacher-embedding supervision (e.g., using teacher embeddings as auxiliary targets or using the teacher to select training pairs), or (b) generating a VariFace dataset with a teacher trained on a disjoint identity set (e.g., MS1MV2 or a different real dataset) and verifying that the gains persist. The paper's own Limitations section (K) acknowledges the reliance on real-data-trained generative models, but does not address the teacher-dependence of the performance claim.
  2. [Tables 3-4; Tables 1-2] All reported accuracies are from single training runs without error bars or significance tests. The central 'outperform real data' result at 1.2M images in Table 3 is a +0.0022 margin (0.9492 vs 0.9470), and the constrained-size Real Gaps (Table 1: -0.0065; Table 2: -0.0008) are close to zero. The paper should provide uncertainty estimates for the key comparisons, e.g., multiple seeds for FR training (at least 2-3) or a paired bootstrap confidence interval over the verification pairs, and phrase 'comparable' vs 'outperform' according to the uncertainty. This is particularly important because the real baseline in Table S5 varies across papers (up to 0.9482), which would shrink the 1.2M margin to +0.0010.
minor comments (6)
  1. [Sec. 4.7, Table 7] The FVSG ablation shows only a small gain on the Standard Benchmark average (0.9399 to 0.9405) and slight decreases on some datasets (e.g., CPLFW 0.8917 to 0.8882, CALFW 0.9313 to 0.9305); please report these inconsistencies or explain why the guidance mainly benefits RFW.
  2. [Sec. 3.1, Fig. S2] CLIP-FRC improves overall race-prediction accuracy only marginally (92.6% to 92.8%) and lowers Caucasian accuracy from 97.1% to 89.4%; the paper should discuss this trade-off and, ideally, show the effect on downstream FR performance of using CLIP vs CLIP-FRC labels.
  3. [Sec. 4.4, Table 3] The 1.2M VariFace comparison uses a dataset with 60K IDs and 20 images per ID, whereas the real baseline uses about 10.5K IDs and 47 images per ID; the paper should state explicitly that this is a scaling comparison and that the real model is trained on the original 0.5M-image dataset.
  4. [Abstract] The phrase 'for the first time, outperforms the real dataset' should be qualified as 'to our knowledge' and clarify that this occurs in the unconstrained (scaled) setting, not at matched dataset size.
  5. [Sec. 4.6] Minor typo: 'Variface's efficiency' should be 'VariFace's efficiency'.
  6. [General] The paper does not state whether the synthetic dataset or generation code will be released; for a dataset-generation paper, making a sample of the generated dataset or the code available would substantially improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: VariFace's central claim is an externally benchmarked empirical result; teacher-model usage is knowledge transfer, not a by-construction reduction.

full rationale

The paper makes no formal derivation that reduces to its inputs. The central claim—that an IResNet-50 trained on 6M VariFace images reaches 0.9567 average accuracy on five standard benchmarks and 0.9366 on RFW—is an empirical result measured on external datasets (LFW, CFP-FP, CPLFW, AgeDB, CALFW, RFW). These benchmarks are not generated by the pipeline and are not used as training labels. The pretrained IResNet-100 is used as an embedding/conditioning source (Secs. 3.1–3.2, Eqs. 1, 3–5, Algorithm 1), but this is a standard knowledge-transfer design: the student network is a different architecture (IResNet-50), is trained from scratch, and its generalization is not guaranteed by the teacher's embeddings. The ablations (Tables 6–8) show that each conditioning signal and each filtering step changes accuracy, so the outcome is not forced by construction. The only author self-citation (Matsunaga et al., 2022) appears as a related-work example of image editing and is not load-bearing. Section K acknowledges the reliance on CASIA-WebFace for training the generative models, but that is a data-dependence limitation, not a circular step. No equation in the paper defines the evaluation metric in terms of the generation conditions, and no fitted parameter is renamed as a prediction. Therefore the appropriate verdict is no significant circularity.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a set of domain assumptions about the reliability of CLIP labels, the structure of FR embedding space, and the transferability of teacher knowledge to a student model. The main fitted parameters are the quality threshold, ID-preservation threshold, DS range (tuned on test benchmarks), K in FRC, and FVSG guidance scale (underspecified). No new physical or mathematical entities are introduced; the 'divergence score' is a simple cosine-similarity metric, not an invented entity.

free parameters (6)
  • Stage 1 quality threshold = 0.7
    CLIB-FIQA quality threshold for filtering stage-1 identities; set empirically (Sec. 4.1).
  • Stage 2 cosine similarity threshold = 0.3
    Filtering threshold for ID preservation, following Vec2Face (Sec. 4.1).
  • Divergence score sampling range = U(0.5, 0.8)
    Selected via a sweep in Table S2 to maximize final benchmark accuracy, a form of test-set tuning.
  • K in Face Recognition Consistency = 50 (main); 10 (RFW evaluation)
    Number of nearest neighbors for label refinement, chosen empirically (Sec. 3.1, Suppl. A.2).
  • FVSG guidance scale = Not specified in main text
    Algorithm 1 includes guidance scale s; Table S1 varies it but the selected value for the final datasets is not stated, leaving a reproducibility gap.
  • Age sampling range = U(0, 1)
    Uniform sampling for age conditioning (Sec. 4.1), not tuned per benchmark.
assumptions (5)
  • domain assumption CLIP provides reliable demographic labels and its embedding space aligns with race, gender, and age categories.
    Used as initial labels and age conditions in Sec. 3.1 and Suppl. A; evaluated in Fig. S2 but with residual errors.
  • domain assumption Pretrained FR embeddings form a meaningful manifold where nearest neighbors share demographic attributes and where cosine distance relates to identity and intraclass variation.
    Used for FRC label refinement (Eq. 1), divergence scores (Eqs. 4-5), and Vendi guidance; if false, the conditioning and filtering collapse.
  • domain assumption Maximizing the Vendi score of a batch of generated face embeddings increases interclass diversity beneficial for FR training.
    Used as a guidance loss (Eq. 3); the paper shows higher FVSG scales increase embedding diversity and reduce quality, but the benefit to downstream FR accuracy is only shown through the ablation in Table 7.
  • domain assumption The diffusion architecture (HDiT) with the specified training schedule can learn to generate high-quality, identity-preserving faces conditioned on the embeddings and scalar labels.
    Arises from prior work; the paper does not prove convergence or quality beyond empirical results.
  • domain assumption Evaluation datasets LFW, CFP-FP, CPLFW, AgeDB, CALFW, and RFW are reliable and unbiased proxies for real-world face recognition performance.
    Used without correction in Sec. 4.2.

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Cite this review

Pith. "Pith review of VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition." pith.science (2026). https://pith.science/paper/ZJ4F265I

@misc{pith2026241206235,
  author       = {Pith},
  title        = {Pith review of: VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZJ4F265I}},
  note         = {Machine review of arXiv:2412.06235}
}
abstract

The use of large-scale, web-scraped datasets to train face recognition models has raised significant privacy and bias concerns. Synthetic methods mitigate these concerns and provide scalable and controllable face generation to enable fair and accurate face recognition. However, existing synthetic datasets display limited intraclass and interclass diversity and do not match the face recognition performance obtained using real datasets. Here, we propose VariFace, a two-stage diffusion-based pipeline to create fair and diverse synthetic face datasets to train face recognition models. Specifically, we introduce three methods: Face Recognition Consistency to refine demographic labels, Face Vendi Score Guidance to improve interclass diversity, and Divergence Score Conditioning to balance the identity preservation-intraclass diversity trade-off. When constrained to the same dataset size, VariFace considerably outperforms previous synthetic datasets (0.9200 $\rightarrow$ 0.9405) and achieves comparable performance to face recognition models trained with real data (Real Gap = -0.0065). In an unconstrained setting, VariFace not only consistently achieves better performance compared to previous synthetic methods across dataset sizes but also, for the first time, outperforms the real dataset (CASIA-WebFace) across six evaluation datasets. This sets a new state-of-the-art performance with an average face verification accuracy of 0.9567 (Real Gap = +0.0097) across LFW, CFP-FP, CPLFW, AgeDB, and CALFW datasets and 0.9366 (Real Gap = +0.0380) on the RFW dataset.

Figures

Figures reproduced from arXiv: 2412.06235 by the authors.

Figure 1
Figure 1. Face verification accuracy using synthetic datasets. Face verification accuracy is the average performance across LFW, CFP-FP, CPLFW, AgeDB, and CALFW datasets. VariFace is trained only with data from CASIA-WebFace (real), the performance of which is shown for reference. All other results are taken from their respective papers. Gururaj et al., 2024). Deep learning performance is limited by training dataset size (Zhu… view at source ↗
Figure 2
Figure 2. VariFace Training and Inference Pipeline. Training: Predictions for race (R*), gender (G*), and age (A) are extracted using a pretrained CLIP model. Next, a pretrained FR model is used to refine race (R) and gender (G) labels, as well as compute identity (ID) embeddings and divergence scores (DS). These labels are used to train conditional diffusion models to generate interclass and intraclass variation in stage 1 a… view at source ↗
Figure 3
Figure 3. Divergence Score Conditioning. By varying the divergence scores applied during sampling, the diversity in generated images can be controlled. From top to bottom, the DS values used are 0.4, 0.6, and 0.8, respectively. All the images are derived from the same synthetic identity. Within the synthetic generation pipeline, where performance is the main concern, we used an IResNet￾100 model, while for evaluation, we used… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Synthetic dataset characteristics. Top: t-SNE plots of mean face embeddings for identities in different synthetic datasets. The race and gender labels for each embedding are represented by different colors defined in the legend. Bottom: Histogram of divergence scores f…

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Forward citations

Cited by 2 Pith papers

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    Thresholds calibrated on synthetic faces transfer poorly to unconstrained real data at low FMR and increase morph-attack acceptance, so high-security deployments still need real-world validation.

  2. Benchmarking Face Recognition without Real Faces

    cs.CV 2026-07 conditional novelty 6.0 of 10

    MorphFace and Vec2Face can replace real photo benchmarks for ranking face recognition models.

Reference graph

Works this paper leans on

82 extracted references · 59 canonical work pages · cited by 2 Pith papers

  1. [1]

    Killing two birds with one stone: Efficient and robust training of face recognition cnns by Partial FC

    Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, and Tongliang Liu. Killing two birds with one stone: Efficient and robust training of face recognition cnns by Partial FC . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 4042--4051, June 2022

    2022

  2. [2]

    DigiFace-1M : 1 million digital face images for face recognition

    Gwangbin Bae, Martin de La Gorce, Tadas Baltru s aitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, and Jingjing Shen. DigiFace-1M : 1 million digital face images for face recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.\ 3526--3535, 2023

    2023

  3. [3]

    SDFD : Building a versatile synthetic face image dataset with diverse attributes

    Georgia Baltsou, Ioannis Sarridis, Christos Koutlis, and Symeon Papadopoulos. SDFD : Building a versatile synthetic face image dataset with diverse attributes. In 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp.\ 1--10, 2024. doi:10.1109/FG59268.2024.10581864

    arXiv 2024

  4. [4]

    Identity-preserving aging of face images via latent diffusion models

    Sudipta Banerjee, Govind Mittal, Ameya Joshi, Chinmay Hegde, and Nasir Memon. Identity-preserving aging of face images via latent diffusion models. In 2023 IEEE International Joint Conference on Biometrics (IJCB), pp.\ 1--10. IEEE, 2023

    work page 2023

  5. [5]

    Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein

    Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL https://openreview.net/forum?id=XH3ArccntI

    work page 2023

  6. [6]

    Universal guidance for diffusion models

    Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Universal guidance for diffusion models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=pzpWBbnwiJ

    work page 2024

  7. [7]

    Sface: Privacy-friendly and accurate face recognition using synthetic data

    Fadi Boutros, Marco Huber, Patrick Siebke, Tim Rieber, and Naser Damer. Sface: Privacy-friendly and accurate face recognition using synthetic data. In 2022 IEEE International Joint Conference on Biometrics (IJCB), pp.\ 1--11. IEEE, 2022

    work page 2022

  8. [8]

    IDiff-Face : Synthetic-based face recognition through fizzy identity-conditioned diffusion model

    Fadi Boutros, Jonas Henry Grebe, Arjan Kuijper, and Naser Damer. IDiff-Face : Synthetic-based face recognition through fizzy identity-conditioned diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 19650--19661, 2023 a

    work page 2023

Show all 82 references
  1. [9]

    ExFaceGAN : Exploring identity directions in GAN’s learned latent space for synthetic identity generation

    Fadi Boutros, Marcel Klemt, Meiling Fang, Arjan Kuijper, and Naser Damer. ExFaceGAN : Exploring identity directions in GAN’s learned latent space for synthetic identity generation. In 2023 IEEE International Joint Conference on Biometrics (IJCB), pp.\ 1--10. IEEE, 2023 b

    2023

  2. [10]

    Synthetic data for face recognition: Current state and future prospects

    Fadi Boutros, Vitomir Struc, Julian Fierrez, and Naser Damer. Synthetic data for face recognition: Current state and future prospects. Image and Vision Computing, 135: 0 104688, 2023 c . ISSN 0262-8856. doi:https://doi.org/10.1016/j.imavis.2023.104688. URL https://www.scienced...

    2023

  3. [11]

    SFace2 : Synthetic-based face recognition with w-space identity-driven sampling

    Fadi Boutros, Marco Huber, Anh Thi Luu, Patrick Siebke, and Naser Damer. SFace2 : Synthetic-based face recognition with w-space identity-driven sampling. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2024

    2024

  4. [12]

    Scalable high-resolution pixel-space image synthesis with hourglass diffusion transformers

    Katherine Crowson, Stefan Andreas Baumann, Alex Birch, Tanishq Mathew Abraham, Daniel Z Kaplan, and Enrico Shippole. Scalable high-resolution pixel-space image synthesis with hourglass diffusion transformers. In Forty-first International Conference on Machine Learning, 2024

    2024

  5. [13]

    FRCSyn challenge at CVPR 2024 : Face recognition challenge in the era of synthetic data

    Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, et al. FRCSyn challenge at CVPR 2024 : Face recognition challenge in the era of synthetic data. In Proce...

    2024

  6. [14]

    ArcFace : Additive angular margin loss for deep face recognition

    Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. ArcFace : Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 4690--4699, 2019

    2019

  7. [15]

    Disentangled and controllable face image generation via 3D imitative-contrastive learning

    Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3D imitative-contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 5154--5163, 2020

    2020

  8. [16]

    Diffusion models beat GANs on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advances in neural information processing systems, 34: 0 8780--8794, 2021

    2021

  9. [17]

    The Vendi score: A diversity evaluation metric for machine learning

    Dan Friedman and Adji Bousso Dieng. The Vendi score: A diversity evaluation metric for machine learning. Transactions on machine learning research, 2023

    2023

  10. [18]

    MS-Celeb-1M : A dataset and benchmark for large-scale face recognition

    Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao. MS-Celeb-1M : A dataset and benchmark for large-scale face recognition. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pp.\ 87...

    2016

  11. [19]

    H. L. Gururaj, B. C. Soundarya, S. Priya, J. Shreyas, and Francesco Flammini. A comprehensive review of face recognition techniques, trends, and challenges. IEEE Access, 12: 0 107903--107926, 2024. doi:10.1109/ACCESS.2024.3424933

    2024

  12. [20]

    Classifier-free diffusion guidance

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022

    2022 arXiv

  13. [21]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33: 0 6840--6851, 2020

    2020

  14. [22]

    Labeled Faces in the Wild : A database for studying face recognition in unconstrained environments

    Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller. Labeled Faces in the Wild : A database for studying face recognition in unconstrained environments. In Workshop on faces in `Real-Life' Images: detection, alignment, and recognition, 2008

    2008

  15. [23]

    CurricularFace : adaptive curriculum learning loss for deep face recognition

    Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, and Feiyue Huang. CurricularFace : adaptive curriculum learning loss for deep face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 5901...

    2020

  16. [24]

    Controllable inversion of black-box face recognition models via diffusion

    Manuel Kansy, Anton Ra \"e l, Graziana Mignone, Jacek Naruniec, Christopher Schroers, Markus Gross, and Romann M Weber. Controllable inversion of black-box face recognition models via diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 3...

    2023

  17. [25]

    FairFace : Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation

    Kimmo Karkkainen and Jungseock Joo. FairFace : Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp.\ 1548--1558, 2021

    2021

  18. [26]

    Training generative adversarial networks with limited data

    Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in neural information processing systems, 33: 0 12104--12114, 2020

    2020

  19. [27]

    The MegaFace benchmark: 1 million faces for recognition at scale

    Ira Kemelmacher-Shlizerman, Steven M Seitz, Daniel Miller, and Evan Brossard. The MegaFace benchmark: 1 million faces for recognition at scale. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 4873--4882, 2016

    2016

  20. [28]

    DiffusionCLIP : Text-guided diffusion models for robust image manipulation

    Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. DiffusionCLIP : Text-guided diffusion models for robust image manipulation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 2426--2435, 2022 a

    2022

  21. [29]

    AdaFace : Quality adaptive margin for face recognition

    Minchul Kim, Anil K Jain, and Xiaoming Liu. AdaFace : Quality adaptive margin for face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 18750--18759, 2022 b

    2022

  22. [30]

    DCFace : Synthetic face generation with dual condition diffusion model

    Minchul Kim, Feng Liu, Anil Jain, and Xiaoming Liu. DCFace : Synthetic face generation with dual condition diffusion model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp.\ 12715--12725, 2023

    2023

  23. [31]

    VIGFace : Virtual identity generation model for face image synthesis

    Minsoo Kim, Min-Cheol Sagong, Gi Pyo Nam, Junghyun Cho, and Ig-Jae Kim. VIGFace : Virtual identity generation model for face image synthesis. arXiv preprint arXiv:2403.08277, 2024

    2024 arXiv

  24. [32]

    Identity-driven three-player generative adversarial network for synthetic-based face recognition

    Jan Niklas Kolf, Tim Rieber, Jurek Elliesen, Fadi Boutros, Arjan Kuijper, and Naser Damer. Identity-driven three-player generative adversarial network for synthetic-based face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp...

    2023

  25. [33]

    Demographic bias effects on face image synthesis

    Roberto Leyva, Victor Sanchez, Gregory Epiphaniou, and Carsten Maple. Demographic bias effects on face image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp.\ 3818--3826, June 2024

    2024

  26. [34]

    a m \"a r \

    Mingrui Li, William A. P. Smith, and Patrik Huber. ID2image : Leakage of non- ID information into face descriptors and inversion from descriptors to images. In Rikke Gade, Michael Felsberg, and Joni-Kristian K \"a m \"a r \"a inen (eds.), Image Analysis, pp.\ 432--448, Cham, 2...

    2023

  27. [35]

    Deep facial expression recognition: A survey

    Shan Li and Weihong Deng. Deep facial expression recognition: A survey. IEEE transactions on affective computing, 13 0 (3): 0 1195--1215, 2020

    2020

  28. [36]

    ID 3 : Identity-preserving-yet-diversified diffusion models for synthetic face recognition

    Shen Li, Jianqing Xu, Jiaying Wu, Miao Xiong, Ailin Deng, Jiazhen Ji, Yuge Huang, Wenjie Feng, Shouhong Ding, and Bryan Hooi. ID 3 : Identity-preserving-yet-diversified diffusion models for synthetic face recognition. arXiv preprint arXiv:2409.17576, 2024

    2024 arXiv

  29. [37]

    Learning to learn across diverse data biases in deep face recognition

    Chang Liu, Xiang Yu, Yi-Hsuan Tsai, Masoud Faraki, Ramin Moslemi, Manmohan Chandraker, and Yun Fu. Learning to learn across diverse data biases in deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 4072--408...

    2022

  30. [38]

    Controllable and guided face synthesis for unconstrained face recognition

    Feng Liu, Minchul Kim, Anil Jain, and Xiaoming Liu. Controllable and guided face synthesis for unconstrained face recognition. In European Conference on Computer Vision, pp.\ 701--719. Springer, 2022 b

    2022

  31. [39]

    SphereFace : Deep hypersphere embedding for face recognition

    Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song. SphereFace : Deep hypersphere embedding for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 212--220, 2017

    2017

  32. [40]

    DPM-Solver++ : Fast solver for guided sampling of diffusion probabilistic models

    Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. DPM-Solver++ : Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095, 2022

    2022 arXiv

  33. [41]

    Fine-grained image editing by pixel-wise guidance using diffusion models

    Naoki Matsunaga, Masato Ishii, Akio Hayakawa, Kenji Suzuki, and Takuya Narihira. Fine-grained image editing by pixel-wise guidance using diffusion models. AI for Content Creation workshop at CVPR2023, 2022

    2022

  34. [42]

    A survey on bias and fairness in machine learning

    Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54 0 (6): 0 1--35, 2021

    2021

  35. [43]

    GANDiffFace : Controllable generation of synthetic datasets for face recognition with realistic variations

    Pietro Melzi, Christian Rathgeb, Ruben Tolosana, Ruben Vera-Rodriguez, Dominik Lawatsch, Florian Domin, and Maxim Schaubert. GANDiffFace : Controllable generation of synthetic datasets for face recognition with realistic variations. In Proceedings of the IEEE/CVF International...

    2023

  36. [44]

    FRCSyn-onGoing : Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

    Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Ivan DeAndres-Tame, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, et al. FRCSyn-onGoing : Benchmarking and comprehensive evaluation of real and synthetic data to improve ...

    2024

  37. [45]

    MagFace : A universal representation for face recognition and quality assessment

    Qiang Meng, Shichao Zhao, Zhida Huang, and Feng Zhou. MagFace : A universal representation for face recognition and quality assessment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 14225--14234, 2021

    2021

  38. [46]

    AgeDB : the first manually collected, in-the-wild age database

    Stylianos Moschoglou, Athanasios Papaioannou, Christos Sagonas, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou. AgeDB : the first manually collected, in-the-wild age database. In proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp.\...

    2017

  39. [47]

    Improved denoising diffusion probabilistic models

    Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International conference on machine learning, pp.\ 8162--8171. PMLR, 2021

    2021

  40. [48]

    CLIB-FIQA : Face image quality assessment with confidence calibration

    Fu-Zhao Ou, Chongyi Li, Shiqi Wang, and Sam Kwong. CLIB-FIQA : Face image quality assessment with confidence calibration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 1694--1704, 2024

    2024

  41. [49]

    Arc2face : A foundation model of human faces

    Foivos Paraperas Papantoniou, Alexandros Lattas, Stylianos Moschoglou, Jiankang Deng, Bernhard Kainz, and Stefanos Zafeiriou. Arc2face : A foundation model of human faces. arXiv preprint arXiv:2403.11641, 2024

    2024 arXiv

  42. [50]

    European Parliament. Regulation ( EU ) 2016/679 of the European Parliament and of the Council on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC . General Data Protection R...

    2016

  43. [51]

    Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence and amending regulations

    European Parliament. Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence and amending regulations. (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives...

    2008

  44. [52]

    Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms

    P Jonathon Phillips, Amy N Yates, Ying Hu, Carina A Hahn, Eilidh Noyes, Kelsey Jackson, Jacqueline G Cavazos, G \'e raldine Jeckeln, Rajeev Ranjan, Swami Sankaranarayanan, et al. Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms...

    2018

  45. [53]

    GlassesGAN : Eyewear personalization using synthetic appearance discovery and targeted subspace modeling

    Richard Plesh, Peter Peer, and Vitomir Struc. GlassesGAN : Eyewear personalization using synthetic appearance discovery and targeted subspace modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 16847--16857, 2023

    2023

  46. [54]

    SynFace : Face recognition with synthetic data

    Haibo Qiu, Baosheng Yu, Dihong Gong, Zhifeng Li, Wei Liu, and Dacheng Tao. SynFace : Face recognition with synthetic data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 10880--10890, 2021

    2021

  47. [55]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp...

    2021

  48. [56]

    Synthetic to authentic: Transferring realism to 3D face renderings for boosting face recognition

    Parsa Rahimi, Behrooz Razeghi, and Sebastien Marcel. Synthetic to authentic: Transferring realism to 3D face renderings for boosting face recognition. arXiv preprint arXiv:2407.07627, 2024

    2024 arXiv

  49. [57]

    3D face reconstruction by learning from synthetic data

    Elad Richardson, Matan Sela, and Ron Kimmel. 3D face reconstruction by learning from synthetic data. In 2016 fourth international conference on 3D vision (3DV), pp.\ 460--469. IEEE, 2016

    2016

  50. [58]

    Learning detailed face reconstruction from a single image

    Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. Learning detailed face reconstruction from a single image. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 5553--5562, 2017. doi:10.1109/CVPR.2017.589

    2017 doi

  51. [59]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj \"o rn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 10684--10695, 2022

    2022

  52. [60]

    Frontal to profile face verification in the wild

    Soumyadip Sengupta, Jun-Cheng Chen, Carlos Castillo, Vishal M Patel, Rama Chellappa, and David W Jacobs. Frontal to profile face verification in the wild. In 2016 IEEE winter conference on applications of computer vision (WACV), pp.\ 1--9. IEEE, 2016

    2016

  53. [61]

    HyperExtended LightFace : A facial attribute analysis framework

    Sefik Ilkin Serengil and Alper Ozpinar. HyperExtended LightFace : A facial attribute analysis framework. In 2021 International Conference on Engineering and Emerging Technologies (ICEET), pp.\ 1--4. IEEE, 2021. doi:10.1109/ICEET53442.2021.9659697. URL https://ieeexplore.ieee.o...

    2021

  54. [62]

    SDFR : Synthetic data for face recognition competition

    Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George, Alexander Unnervik, S \'e bastien Marcel, Nicol \`o Di Domenico, Guido Borghi, Davide Maltoni, Fadi Boutros, Julia Vogel, et al. SDFR : Synthetic data for face recognition competition. In 2024 IEEE 18th International C...

    2024

  55. [63]

    GLU variants improve transformer

    Noam Shazeer. GLU variants improve transformer. arXiv preprint arXiv:2002.05202, 2020

    2002 arXiv

  56. [64]

    InterFaceGAN : Interpreting the disentangled face representation learned by GANs

    Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. InterFaceGAN : Interpreting the disentangled face representation learned by GANs . IEEE transactions on pattern analysis and machine intelligence, 44 0 (4): 0 2004--2018, 2020

    2004

  57. [65]

    Deep learning face representation by joint identification-verification

    Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang. Deep learning face representation by joint identification-verification. Advances in neural information processing systems, 27, 2014

    2014

  58. [66]

    Dataset cartography: Mapping and diagnosing datasets with training dynamics

    Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. Dataset cartography: Mapping and diagnosing datasets with training dynamics. arXiv preprint arXiv:2009.10795, 2020

    2009 arXiv

  59. [67]

    DeepFace : Closing the gap to human-level performance in face verification

    Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. DeepFace : Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 1701--1708, 2014

    2014

  60. [68]

    Beyond skin tone: A multidimensional measure of apparent skin color

    William Thong, Przemyslaw Joniak, and Alice Xiang. Beyond skin tone: A multidimensional measure of apparent skin color. In ICCV, 2023

    2023

  61. [69]

    Generating photo-realistic training data to improve face recognition accuracy

    Daniel S \'a ez Trigueros, Li Meng, and Margaret Hartnett. Generating photo-realistic training data to improve face recognition accuracy. Neural Networks, 134: 0 86--94, 2021

    2021

  62. [70]

    Exploring CLIP for assessing the look and feel of images

    Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring CLIP for assessing the look and feel of images. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pp.\ 2555--2563, 2023

    2023

  63. [71]

    Deep face recognition: A survey

    Mei Wang and Weihong Deng. Deep face recognition: A survey. Neurocomputing, 429: 0 215--244, 2021

    2021

  64. [72]

    Racial Faces in the Wild : Reducing racial bias by information maximization adaptation network

    Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, and Yaohai Huang. Racial Faces in the Wild : Reducing racial bias by information maximization adaptation network. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 692--702, 2019

    2019

  65. [73]

    Fake it till you make it: face analysis in the wild using synthetic data alone

    Erroll Wood, Tadas Baltru s aitis, Charlie Hewitt, Sebastian Dziadzio, Thomas J Cashman, and Jamie Shotton. Fake it till you make it: face analysis in the wild using synthetic data alone. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 3681--3691, 2021

    2021

  66. [74]

    Vec2Face : Scaling face dataset generation with loosely constrained vectors

    Haiyu Wu, Jaskirat Singh, Sicong Tian, Liang Zheng, and Kevin W Bowyer. Vec2Face : Scaling face dataset generation with loosely constrained vectors. arXiv preprint arXiv:2409.02979, 2024 a

    2024 arXiv

  67. [75]

    Text-guided 3D face synthesis-from generation to editing

    Yunjie Wu, Yapeng Meng, Zhipeng Hu, Lincheng Li, Haoqian Wu, Kun Zhou, Weiwei Xu, and Xin Yu. Text-guided 3D face synthesis-from generation to editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 1260--1269, 2024 b

    2024

  68. [76]

    Demystifying CLIP data

    Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer. Demystifying CLIP data. arXiv preprint arXiv:2309.16671, 2023

    2023 arXiv

  69. [77]

    Learning face representation from scratch

    Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014

    2014 arXiv

  70. [78]

    Cross-Pose LFW : A database for studying cross-pose face recognition in unconstrained environments

    Tianyue Zheng and Weihong Deng. Cross-Pose LFW : A database for studying cross-pose face recognition in unconstrained environments. Beijing University of Posts and Telecommunications, Tech. Rep, 5 0 (7): 0 5, 2018

    2018

  71. [79]

    Cross-Age LFW : A database for studying cross-age face recognition in unconstrained environments

    Tianyue Zheng, Weihong Deng, and Jiani Hu. Cross-Age LFW : A database for studying cross-age face recognition in unconstrained environments. arXiv preprint arXiv:1708.08197, 2017

    2017 arXiv

  72. [80]

    UniFace : Unified cross-entropy loss for deep face recognition

    Jiancan Zhou, Xi Jia, Qiufu Li, Linlin Shen, and Jinming Duan. UniFace : Unified cross-entropy loss for deep face recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 20730--20739, 2023

    2023

  73. [81]

    WebFace260M : A benchmark unveiling the power of million-scale deep face recognition

    Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, and Jie Zhou. WebFace260M : A benchmark unveiling the power of million-scale deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision...

    2021

  74. [82]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.