论文标题
FaceOCC：人脸提取的多样化，高质量的脸部闭塞数据集
FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction
论文作者
论文摘要
闭塞通常发生在野外的，令人不安的面部相关任务中，例如里程碑式检测，3D重建和面部识别。准确地从无约束的面部图像中提取面部区域是有益的。但是，当前的面部分割数据集遭受了少量数据量，很少的遮挡类型，低分辨率和不精确的注释，从而限制了基于数据驱动的算法的性能。本文提出了一个新颖的面部遮挡数据集，并带有Celeba-HQ和Internet的手动标记的脸部遮挡。闭塞类型涵盖了太阳镜，眼镜，手，口罩，围巾，麦克风等。据我们所知，它是迄今为止最大，最全面的遮挡数据集。将其与Celebamask-HQ中的属性掩码相结合，我们训练了一个直接的面部分割模型，但获得了SOTA性能，令人信服地证明了所提出的数据集的有效性。
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.
