Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild

TL;DR

This paper introduces large-scale, diverse face and non-face image datasets with variations in occlusion, pose, and environment to enhance face detection and recognition in unconstrained settings.

Contribution

The paper presents the largest labeled face dataset with diverse variations and new crowd scene datasets for improved large-scale face learning and recognition.

Findings

Largest labeled face dataset with occlusions and variations

Datasets include diverse conditions like pose, illumination, and accessories

Facilitates training of more robust face detection models

Abstract

Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of unconstrained multi-view and partially occluded faces. The faces have many variations in color and grayscale, image quality, image resolution, image illumination, image background, image illusion, human face, cartoon face, facial expression, light and severe partial facial occlusion, make up, gender, age, and race. Many of these faces are partially occluded with accessories such as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves, microphones, or other objects or persons. The LSLF dataset is currently the largest labeled face image dataset in the literature in terms of the number of labeled images and the number of individuals compared to other existing labeled face image datasets. Second, we introduce our CrowedFaces and CrowedNonFaces image datasets. The crowedFaces and CrowedNonFaces datasets include faces and non-faces images from crowed scenes. These datasets essentially aim for researchers to provide a large number of training examples with many variations for large scale face learning and face recognition tasks.