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Face detection methods have relied on face datasets for training. However,existing face datasets tend to be in small scales for face learning in bothconstrained and unconstrained environments. In this paper, we first introduceour large-scale image datasets, Large-scale Labeled Face (LSLF) and noisyLarge-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a largenumber of unconstrained multi-view and partially occluded faces. The faces havemany variations in color and grayscale, image quality, image resolution, imageillumination, 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 suchas tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves,microphones, or other objects or persons. The LSLF dataset is currently thelargest labeled face image dataset in the literature in terms of the number oflabeled images and the number of individuals compared to other existing labeledface image datasets. Second, we introduce our CrowedFaces and CrowedNonFacesimage datasets. The crowedFaces and CrowedNonFaces datasets include faces andnon-faces images from crowed scenes. These datasets essentially aim forresearchers to provide a large number of training examples with many variationsfor large scale face learning and face recognition tasks.

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