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