FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction

Related tags

Deep Learning FaceExtraction
Overview

FaceExtraction

FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction

Occlusions often occur in face images in the wild, troubling face-related tasks such as landmark detection, 3D reconstruction, and face recognition. It is beneficial to extract face regions from unconstrained face images accurately. However, current face segmentation datasets suffer from small data volumes, few occlusion types, low resolution, and imprecise annotation, limiting the performance of data-driven-based algorithms. This paper proposes a novel face occlusion dataset with manually labeled face occlusions from the CelebA-HQ and the internet. The occlusion types cover sunglasses, spectacles, hands, masks, scarfs, microphones, etc. To the best of our knowledge, it is by far the largest and most comprehensive face occlusion dataset. Combining it with the attribute mask in CelebAMask-HQ, we trained a straightforward face segmentation model but obtained SOTA performance, convincingly demonstrating the effectiveness of the proposed dataset.

Requirements

How to use

  1. Download CelebAMask-HQ dataset, detect the facial landmarks using 3DDFAv2
  2. Specify the directories in face_align/process_CelebAMaskHQ.py
  3. Run face_align/process_CelebAMaskHQ.py to generate&align CelebAMask-HQ images and masks 4.Download FaceOcc and put it under Dataset directory 5.Run train.py

Dataset

FaceOcc

Pretrained Model

Pretrained Model

Results

Face masks are shown in blue. From top to bottom are input images, predicted masks, and the ground truth: From top to the bottom: input images, predicted masks, ground truth

Citation

@misc{yin2022faceocc,
      title={FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction}, 
      author={Xiangnan Yin and Liming Chen},
      year={2022},
      eprint={2201.08425},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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Comments
  • additional explanation for landmark

    additional explanation for landmark

    Hi I have seen your great code.

    I have a question, I need a facial landmark to run face_align/process_CelebAMaskHQ.py for train. You said to use 3DDFAv2, but there is no output in the form of a pickle file. This part needs a more detailed explanation.

    opened by leeisack 0
  • Landmark issue

    Landmark issue

    Hi, this is an interesting work and I am trying to reproduce it. For detecting the facial landmarks using [3DDFAv2], there are different opts like 2d_sparse, 3d, etc. Which one do you use in the project?

    opened by hotfinda 0
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