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BooBooWu/
Vec2Face

Name: Vec2Face
Rating: 2.5 (165 reviews)

unconditional-image-generation

2409.02979

mit

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Vec2Face Model Card

Project Page | Paper | Code | 🤗 Gradio demo

Introduction

Vec2Face is the first model that achieves the generated synthetic face recognition dataset (HSFace10K) first being higher than the same-scale real dataset (CASIA-WebFace).

Usage

You can directly download the model in this repository. You also can download the model in python script:

python

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="BooBooWu/Vec2Face", filename="weights/6DRepNet_300W_LP_AFLW2000.pth", local_dir="./")
hf_hub_download(repo_id="BooBooWu/Vec2Face", filename="weights/arcface-r100-glint360k.pth", local_dir="./")
hf_hub_download(repo_id="BooBooWu/Vec2Face", filename="weights/magface-r100-glint360k.pth", local_dir="./")
hf_hub_download(repo_id="BooBooWu/Vec2Face", filename="weights/vec2face_generator.pth", local_dir="./")

For the image generation and training code, please go to Vec2Face github repository.

Performance

Datasets in 0.5M scale

This table compares the existing synthetic dataset generation methods on five standard face recognition test sets. The model trained with HSFace10K has better performance on CALFW than that trained with real dataset.

Training sets	# images	LFW	CFP-FP	CPLFW	AgeDB	CALFW	Avg.
IDiff-Face	0.5M	98.00	85.47	80.45	86.43	90.65	88.20
DCFace	0.5M	98.55	85.33	82.62	89.70	91.60	89.56
Arc2Face	0.5M	98.81	91.87	85.16	90.18	92.63	91.73
DigiFace	1M	95.40	87.40	78.87	76.97	78.62	83.45
SynFace	0.5M	91.93	75.03	70.43	61.63	74.73	74.75
SFace	0.6M	91.87	73.86	73.20	71.68	77.93	77.71
IDnet	0.5M	92.58	75.40	74.25	63.88	79.90	79.13
ExFaceGAN	0.5M	93.50	73.84	71.60	78.92	82.98	80.17
SFace2	0.6M	95.60	77.11	74.60	77.37	83.40	81.62
Langevin-Disco	0.6M	96.60	73.89	74.77	80.70	87.77	82.75
HSFace10K(Ours)	0.5M	98.87	88.97	85.47	93.12	93.57	92.00
CASIA-WebFace (Real)	0.49M	99.38	96.91	89.78	94.50	93.35	94.79

###Dataset Scaling up to 300K identities and 15M images

This is the uniqueness of the proposed Vec2Face, which can easily scale the dataset size up.

Datasets	# images	LFW	CFP-FP	CPLFW	AgeDB	CALFW	Avg.
HSFace10K	0.5M	98.87	88.97	85.47	93.12	93.57	92.00
HSFace20K	1M	98.87	89.87	86.13	93.85	93.65	92.47
HSFace100K	5M	99.25	90.36	86.75	94.38	94.12	92.97
HSFace200K	10M	99.23	90.81	87.30	94.22	94.52	93.22
HSFace300K	15M	99.30	91.54	87.70	94.45	94.58	93.52
CASIA-WebFace (Real)	0.49M	99.38	96.91	89.78	94.50	93.35	94.79

Other challenging test sets

We test the model performance on other four datasets, Hadrian (facial hair), Eclipse (face exposure), SLLFW (similar-looking), and DoppelVer (doppelganger).

Datasets	Hadrian	Eclipse	SLLFW	DoppelVer
HSFace10K	69.47	64.55	92.87	86.91
HSFace20K	75.22	67.55	94.37	88.91
HSFace100K	80.00	70.35	95.58	90.39
HSFace200K	79.85	71.12	95.70	89.86
HSFace300K	81.55	71.35	95.95	90.49
CASIA-WebFace (Real)	77.82	68.52	96.95	95.11

Citation

bibtex

@article{wu2024vec2face,
  title={Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors},
  author={Wu, Haiyu and Singh, Jaskirat and Tian, Sicong and Zheng, Liang and Bowyer, Kevin W},
  journal={arXiv preprint arXiv:2409.02979},
  year={2024}
}