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

TL;DR

This paper introduces FaceOcc, a large and diverse face occlusion dataset with detailed annotations, enabling significant improvements in face segmentation tasks under occlusion conditions.

Contribution

The creation of the largest, most comprehensive face occlusion dataset with diverse occlusion types and precise labels, enhancing face segmentation performance.

Findings

Achieved state-of-the-art face segmentation results using the new dataset.

Demonstrated the dataset's effectiveness across multiple occlusion scenarios.

Improved robustness of face extraction methods in real-world conditions.

Abstract

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.