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

Tarik Alafif, Zeyad Hailat, Melih Aslan, Xuewen Chen

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.

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