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TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective

If you like TransFace, please give us a star ⭐ on GitHub for the latest update~

This is the official PyTorch implementation of [ICCV-2023] TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective.

[Arxiv Version]

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News

  • 🚀🚀🚀 TransFace is integrated in FaceChain-FACT as a key identity-preserved module to assist Stable Diffusion in generating human portraits with fine-grained facial details and diverse styles. In the newest FaceChain-FACT (Face Adapter with deCoupled Training) version, with only 1 photo and 10 seconds, you can generate personal portraits in different settings (multiple styles now supported!). (May 28th, 2024 UTC)

YouTube

The entire framework of FaceChain-FACT is shown in the figure below.

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ModelScope

You can quickly experience and invoke our TransFace model on the ModelScope.

  • Quickly utilize our model as a feature extractor to extract facial features from the input image.
# Usage: Input aligned facial images (112x112) to obtain a 512-dimensional facial feature vector.
# For convenience, the model integrates the RetinaFace model for face detection and keypoint estimation.
# Provide two images as input, and for each image, the model will independently perform face detection,
# select the largest face, align it, and extract the corresponding facial features.
# Finally, the model will return a similarity score indicating the resemblance between the two faces.

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.outputs import OutputKeys
import numpy as np

face_mask_recognition_func = pipeline(Tasks.face_recognition, 'damo/cv_vit_face-recognition')
img1 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_1.png'
img2 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_2.png'
emb1 = face_mask_recognition_func(img1)[OutputKeys.IMG_EMBEDDING]
emb2 = face_mask_recognition_func(img2)[OutputKeys.IMG_EMBEDDING]
sim = np.dot(emb1[0], emb2[0])
print(f'Face cosine similarity={sim:.3f}, img1:{img1}  img2:{img2}')

Requirements

  • Install Pytorch (torch>=1.9.0)
  • pip install -r requirement.txt

Datasets

You can download the training datasets, including MS1MV2 and Glint360K:

You can download the test dataset IJB-C as follows:

How to Train Models

  1. You need to modify the path of training data in every configuration file in folder configs.

  2. To run on a machine with 8 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py

How to Test Models

  1. You need to modify the path of IJB-C dataset in eval_ijbc.py.

  2. Run:

python eval_ijbc.py --model-prefix work_dirs/glint360k_vit_s/model.pt --result-dir work_dirs/glint360k_vit_s --network vit_s_dp005_mask_0 > ijbc_glint360k_vit_s.log 2>&1 &

TransFace Pretrained Models

You can download the TransFace models reported in our paper as follows:

Training Data Model IJB-C(1e-6) IJB-C(1e-5) IJB-C(1e-4) IJB-C(1e-3) IJB-C(1e-2) IJB-C(1e-1)
MS1MV2 TransFace-S 86.75 93.87 96.45 97.51 98.34 98.99
MS1MV2 TransFace-B 86.73 94.15 96.55 97.73 98.47 99.11
MS1MV2 TransFace-L 86.90 94.55 96.59 97.80 98.45 99.04
Training Data Model IJB-C(1e-6) IJB-C(1e-5) IJB-C(1e-4) IJB-C(1e-3) IJB-C(1e-2) IJB-C(1e-1)
Glint360K TransFace-S 89.93 96.06 97.33 98.00 98.49 99.11
Glint360K TransFace-B 88.64 96.18 97.45 98.17 98.66 99.23
Glint360K TransFace-L 89.71 96.29 97.61 98.26 98.64 99.19

You can test the accuracy of these model: (e.g. Glint360K TransFace-L)

python eval_ijbc.py --model-prefix work_dirs/glint360k_vit_l/glint360k_model_TransFace_L.pt --result-dir work_dirs/glint360k_vit_l --network vit_l_dp005_mask_005 > ijbc_glint360k_vit_l.log 2>&1 &

Citation

  • If you find it helpful for you, please consider citing our paper 📝 and giving a star ⭐.
@inproceedings{dan2023transface,
  title={TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective},
  author={Dan, Jun and Liu, Yang and Xie, Haoyu and Deng, Jiankang and Xie, Haoran and Xie, Xuansong and Sun, Baigui},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={20642--20653},
  year={2023}
}

Acknowledgments

We thank Insighface for the excellent code base.