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AdaFace: Quality Adaptive Margin for Face Recognition

Official github repository for AdaFace: Quality Adaptive Margin for Face Recognition. The paper (https://arxiv.org/abs/2204.00964) is presented in CVPR 2022 (Oral).

Abstract: Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy and hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulty based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace).

@inproceedings{kim2022adaface,
  title={AdaFace: Quality Adaptive Margin for Face Recognition},
  author={Kim, Minchul and Jain, Anil K and Liu, Xiaoming},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

PWC PWC PWC PWC PWC

Repository Updates

  • CVLFace is the new official repository for AdaFace (supports various architectures such as ViT, SWIN-ViT, KP-RPE, etc).
  • Added PartialFC AdaFace implementation at CVLFace (and much more functionalities related to face recognition for conducting research).
  • Pytorch Lightning 1.8 compatibility is tested.
  • 5 Minute video presentation uploaded.
  • Added the option to directly train with InsightFace dataset (train.rec) files without extracting images.

News

  • You can also check out our new paper KP-RPE: KeyPoint Relative Position Encoding for Face Recognition Paper Video Code for facial landmark assisted face recognition. TLDR: Face recognition with facial landmark for alignment robustness. (SoTA in TinyFace, IJB-S).
  • You can also check out our new paper Cluster and Aggregate (CAFace, NeurIPS2022) Link for video based face recognition. TLDR: Face recognition with long probe videos.

5 Minute Video Presentation

IMAGE ALT TEXT

Demo Comparison between AdaFace and ArcFace on Low Quality Images

Demo The demo shows a comparison between AdaFace and ArcFace on a live video. To show how model performs with low quality images, we show original, blur+ and blur++ setting where blur++ means it is heavily blurred. The numbers with colorbox show the cosine similarity between the live image and the cloest matching gallery image. The statistics on the bottom show the cumulative count of true positive match for blur++ setting. AdaFace has high true positive rate. It also shows it is less prone to making false positive (red) mistakes as sometimes observed in ArcFace.

Usage

import torch
from head import AdaFace

# typical inputs with 512 dimension
B = 5
embbedings = torch.randn((B, 512)).float()  # latent code
norms = torch.norm(embbedings, 2, -1, keepdim=True)
normalized_embedding  = embbedings / norms
labels =  torch.randint(70722, (B,))

# instantiate AdaFace
adaface = AdaFace(embedding_size=512,
                  classnum=70722,
                  m=0.4,
                  h=0.333,
                  s=64.,
                  t_alpha=0.01,)

# calculate loss
cosine_with_margin = adaface(normalized_embedding, norms, labels)
loss = torch.nn.CrossEntropyLoss()(cosine_with_margin, labels)

Installation

conda create --name adaface pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
conda activate adaface
conda install scikit-image matplotlib pandas scikit-learn 
pip install -r requirements.txt

Train (Preapring Dataset and Training Scripts)

  • Please refer to README_TRAIN.md
  • [IMPORTANT] Note that our implementation assumes that input to the model is BGR color channel as in cv2 package. InsightFace models assume RGB color channel as in PIL package. So all our evaluation code uses BGR color channel with cv2 package.

Pretrained Models

Note that our pretrained model takes the input in BGR color channel. This is different from the InsightFace released model which uses RGB color channel.

Arch Dataset Link
R18 CASIA-WebFace gdrive
R18 VGGFace2 gdrive
R18 WebFace4M gdrive
R50 CASIA-WebFace gdrive
R50 WebFace4M gdrive
R50 MS1MV2 gdrive
R100 MS1MV2 gdrive
R100 MS1MV3 gdrive
R100 WebFace4M gdrive
R100 WebFace12M gdrive

Inferece

Example using provided sample images

AdaFace takes input images that are preproccsed. The preprocessing step involves

  1. aligned with facial landmark (using MTCNN) and
  2. cropped to 112x112x3 size whose color channel is BGR order.

We provide the code for performing the preprocessing step. For using pretrained AdaFace model for inference,

  1. Download the pretrained adaface model and place it in pretrained/

  2. For using pretrained AdaFace on below 3 images, run

python inference.py
img1 img2 img3

The similarity score result should be

tensor([[ 1.0000,  0.7334, -0.0655],
        [ 0.7334,  1.0000, -0.0277],
        [-0.0655, -0.0277,  1.0000]], grad_fn=<MmBackward0>)

General Inference Guideline

In a nutshell, inference code looks as below.

from face_alignment import align
from inference import load_pretrained_model, to_input

model = load_pretrained_model('ir_50')
path = 'path_to_the_image'
aligned_rgb_img = align.get_aligned_face(path)
bgr_input = to_input(aligned_rgb_img)
feature, _ = model(bgr_input)
  • Note that AdaFace model is a vanilla pytorch model which takes in bgr_input which is 112x112x3 torch tensor with BGR color channel whose value is normalized with mean=0.5 and std=0.5, as in to_input()
  • When preprocessing step produces error, it is likely that the MTCNN cannot find face in an image. Refer to issues/28 for the discussion.

Validation

High Quality Image Validation Sets (LFW, CFPFP, CPLFW, CALFW, AGEDB)

For evaluation on 5 HQ image validation sets with pretrained models, refer to

bash validation_hq/eval_5valsets.sh
Arch Dataset Method LFW CFPFP CPLFW CALFW AGEDB AVG
R18 CASIA-WebFace AdaFace 0.9913 0.9259 0.8700 0.9265 0.9272 0.9282
R18 VGGFace2 AdaFace 0.9947 0.9713 0.9172 0.9390 0.9407 0.9526
R18 WebFace4M AdaFace 0.9953 0.9726 0.9228 0.9552 0.9647 0.9621
R50 CASIA-WebFace AdaFace 0.9942 0.9641 0.8997 0.9323 0.9438 0.9468
R50 MS1MV2 AdaFace 0.9982 0.9786 0.9283 0.9607 0.9785 0.9688
R50 WebFace4M AdaFace 0.9978 0.9897 0.9417 0.9598 0.9778 0.9734
R100 MS1MV2 AdaFace 0.9982 0.9849 0.9353 0.9608 0.9805 0.9719
R100 MS1MV3 AdaFace 0.9978 0.9891 0.9393 0.9602 0.9817 0.9736
R100 WebFace4M AdaFace 0.9980 0.9917 0.9463 0.9605 0.9790 0.9751
R100 WebFace12M AdaFace 0.9982 0.9926 0.9457 0.9612 0.9800 0.9755

Comparison with Other Methods

Arch Dataset Method LFW CFPFP CPLFW CALFW AGEDB AVG
R50 CASIA-WebFace AdaFace 0.9942 0.9641 0.8997 0.9323 0.9438 0.9468
R50 CASIA-WebFace (ArcFace) 0.9945 0.9521 NA NA 0.9490 NA
R100 MS1MV2 AdaFace 0.9982 0.9849 0.9353 0.9608 0.9805 0.9719
R100 MS1MV2 (ArcFace) 0.9982 NA 0.9208 0.9545 NA NA

Mixed Quality Scenario (IJBB, IJBC Dataset)

For IJBB, IJBC validation, refer to

cd validation_mixed
bash eval_ijb.sh
Arch Dataset Method IJBB TAR@FAR=0.01% IJBC TAR@FAR=0.01%
R18 VGG2 AdaFace 90.67 92.95
R18 WebFace4M AdaFace 93.03 94.99
R50 WebFace4M AdaFace 95.44 96.98
R50 MS1MV2 AdaFace 94.82 96.27
R100 MS1MV2 AdaFace 95.67 96.89
R100 MS1MV3 AdaFace 95.84 97.09
R100 WebFace4M AdaFace 96.03 97.39
R100 WebFace12M AdaFace 96.41 97.66

Comparison with Other Methods

  • Numbers for other methods come from their respective papers.
Arch Dataset Method Venue IJBB TAR@FAR=0.01% IJBC TAR@FAR=0.01%
R100 MS1MV2 AdaFace CVPR22 95.67 96.89
R100 MS1MV2 (MagFace) CVPR21 94.51 95.97
R100 MS1MV2 (SCF-ArcFace) CVPR21 94.74 96.09
R100 MS1MV2 (BroadFace) ECCV20 94.97 96.38
R100 MS1MV2 (CurricularFace) CVPR20 94.80 96.10
R100 MS1MV2 (MV-Softmax) AAAI20 93.60 95.20
R100 MS1MV2 (AFRN) ICCV19 88.50 93.00
R100 MS1MV2 (ArcFace) CVPR19 94.25 96.03
R100 MS1MV2 (CosFace) CVPR18 94.80 96.37
Arch Dataset Method IJBC TAR@FAR=0.01%
R100 WebFace4M AdaFace 97.39
R100 WebFace4M (CosFace) 96.86
R100 WebFace4M (ArcFace) 96.77
R100 WebFace4M (CurricularFace) 97.02
Arch Dataset Method IJBC TAR@FAR=0.01%
R100 WebFace12M AdaFace 97.66
R100 WebFace12M (CosFace) 97.41
R100 WebFace12M (ArcFace) 97.47
R100 WebFace12M (CurricularFace) 97.51

Low Quality Scenario (IJBS)

For IJBB, IJBC validation, refer to

cd validation_lq
python validate_IJB_S.py

Comparison with Other Methods

Sur-to-Single Sur-to-Book Sur-to-Sur TinyFace
Arch Method Dataset Rank1 Rank5 1% Rank1 Rank5 1% Rank1 Rank5 1% rank1 rank5
R100 AdaFace WebFace12M 71.35 76.24 59.39 71.93 76.56 59.37 36.71 50.03 4.62 72.29 74.97
R100 AdaFace WebFace4M 70.42 75.29 58.27 70.93 76.11 58.02 35.05 48.22 4.96 72.02 74.52
R100 AdaFace MS1MV2 65.26 70.53 51.66 66.27 71.61 50.87 23.74 37.47 2.50 68.21 71.54
R100 (CurricularFace) MS1MV2 62.43 68.68 47.68 63.81 69.74 47.57 19.54 32.80 2.53 63.68 67.65
R100 (URL) MS1MV2 58.94 65.48 37.57 61.98 67.12 42.73 NA NA NA 63.89 68.67
R100 (ArcFace) MS1MV2 57.35 64.42 41.85 57.36 64.95 41.23 NA NA NA NA NA
R100 (PFE) MS1MV2 50.16 58.33 31.88 53.60 61.75 35.99 9.20 20.82 0.84 NA NA
  • Sur-to-Single: Protocol comparing surveillance video (probe) to single enrollment image (gallery)
  • Sur-to-Book: Protocol comparing surveillance video (probe) to all enrollment images (gallery)
  • Sur-to-Sur: Protocol comparing surveillance video (probe) to surveillance video (gallery)