{"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/afif4-deep-gender-classification-based-on","title":"AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces","arxiv_id":"1706.04277","date":"2017-06-13","proceeding":null,"authors":["Mahmoud Afifi","Abdelrahman Abdelhamed"],"abstract":"Gender classification aims at recognizing a person's gender. Despite the high\naccuracy achieved by state-of-the-art methods for this task, there is still\nroom for improvement in generalized and unrestricted datasets. In this paper,\nwe advocate a new strategy inspired by the behavior of humans in gender\nrecognition. Instead of dealing with the face image as a sole feature, we rely\non the combination of isolated facial features and a holistic feature which we\ncall the foggy face. Then, we use these features to train deep convolutional\nneural networks followed by an AdaBoost-based score fusion to infer the final\ngender class. We evaluate our method on four challenging datasets to\ndemonstrate its efficacy in achieving better or on-par accuracy with\nstate-of-the-art methods. In addition, we present a new face dataset that\nintensifies the challenges of occluded faces and illumination changes, which we\nbelieve to be a much-needed resource for gender classification research.","url_abs":"http://arxiv.org/abs/1706.04277v5","url_pdf":"http://arxiv.org/pdf/1706.04277v5.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":"gender-classification","task_name":"Gender Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"sof","name":"SoF","full_name":"Specs on Faces"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}