Ethnic group classifier

ResNet-18-based classifier of face ethnicity into 4 classes (African, East Asian, Indian, Caucasian) that was used in the paper:

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Artem Sevastopolsky, Yury Malkov, Nikita Durasov, Luisa Verdoliva, Matthias Nießner. How to Boost Face Recognition with StyleGAN? // International Conference on Computer Vision (ICCV), 2023

Project page   Paper   Video   Code

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The model was trained in 2 stages:

1. Training ResNet-18 (from ImageNet checkpoint) on images from BUPT-BalancedFace and corresponding ethnicity labels. We noticed that after this stage, the model performs reasonably well but still makes consistent mistakes.
2. Inferring the model on WebFace-42M dataset (containing many images of the same people) and applying consensus procedure: considering an identity of ethnicity X, if and only if for >= 80% of this identity's images, the classifier predicted the race X (not more than 20 random images per identity considered). Training a new classifier on the subset of selected ethnicities WebFace-42M and corresponding labels.

The identities list can be found in the data released for the paper and is accessible by filling in the form (see stylegan-for-facerec for more details).

This code only provides the inference interface.

Running

1. Set up the environment: pytorch, torchvision, pytorch-lightning, matplotlib, einops, tqdm. For alignment (step 3), download the source of mtcnn-pytorch in a separate folder and install its dependencies.
2. Download the checkpoint from here and put it in pretrained_models folder.
3. If required, align & crop the images in your folder input_dir via:

python facesets/mtcnn_crop_align.py \
    --in_dir <input_dir> \
    --out_dir <output folder with cropped and aligned images> \
    --mtcnn_pytorch_path <path, to which mtcnn-pytorch source has been downloaded> \
    --n_threads <number of parallel threads on the same GPU>

The resulting images should have 112x112 resolution and be in alignment with each other by facial landmarks.

4. Run the ethnicity classifier inference:

python infer.py \
    --input_dir <aligned & cropped input images> \
    --output_dir <output dir> \
    --ckpt pretrained_models/resnet18.ckpt \
    [--visualize] \
    [--n_vis_samples_per_pic <if --visualize, this defines how many samples should each plot contain>] \
    [--n_vis_rows_per_pic <if --visualize, this defines how many rows should each plot contain -- must divide --n_vis_samples_per_pic>] \ 
    [--figsize_h <fig height>] [--figsize_w <fig width>]

The output dir will contain a file pred_ethnicities.txt with space-delimited pairs <filename> <ethnicity>. If --visualize option is selected, grids of images with corresponding predictions will also be added there.