{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/large-scale-datasets-faces-with-partial","title":"Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild","arxiv_id":"1706.08690","date":"2017-06-27","proceeding":null,"authors":["Tarik Alafif","Zeyad Hailat","Melih Aslan","Xue-wen Chen"],"abstract":"Face detection methods have relied on face datasets for training. However,\nexisting face datasets tend to be in small scales for face learning in both\nconstrained and unconstrained environments. In this paper, we first introduce\nour large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy\nLarge-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large\nnumber of unconstrained multi-view and partially occluded faces. The faces have\nmany variations in color and grayscale, image quality, image resolution, image\nillumination, image background, image illusion, human face, cartoon face,\nfacial expression, light and severe partial facial occlusion, make up, gender,\nage, and race. Many of these faces are partially occluded with accessories such\nas tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves,\nmicrophones, or other objects or persons. The LSLF dataset is currently the\nlargest labeled face image dataset in the literature in terms of the number of\nlabeled images and the number of individuals compared to other existing labeled\nface image datasets. Second, we introduce our CrowedFaces and CrowedNonFaces\nimage datasets. The crowedFaces and CrowedNonFaces datasets include faces and\nnon-faces images from crowed scenes. These datasets essentially aim for\nresearchers to provide a large number of training examples with many variations\nfor large scale face learning and face recognition tasks.","url_abs":"http://arxiv.org/abs/1706.08690v1","url_pdf":"http://arxiv.org/pdf/1706.08690v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"face-recognition","task_name":"Face Recognition"}],"methods":[],"datasets_introduced":[{"slug":"lslf","name":"LSLF","full_name":"Large-scale Labeled Face"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}