{"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/robust-and-low-rank-representation-for-fast","title":"Robust and Low-Rank Representation for Fast Face Identification with Occlusions","arxiv_id":"1605.02266","date":"2016-05-08","proceeding":null,"authors":["Michael Iliadis","Haohong Wang","Rafael Molina","Aggelos K. Katsaggelos"],"abstract":"In this paper we propose an iterative method to address the face\nidentification problem with block occlusions. Our approach utilizes a robust\nrepresentation based on two characteristics in order to model contiguous errors\n(e.g., block occlusion) effectively. The first fits to the errors a\ndistribution described by a tailored loss function. The second describes the\nerror image as having a specific structure (resulting in low-rank in comparison\nto image size). We will show that this joint characterization is effective for\ndescribing errors with spatial continuity. Our approach is computationally\nefficient due to the utilization of the Alternating Direction Method of\nMultipliers (ADMM). A special case of our fast iterative algorithm leads to the\nrobust representation method which is normally used to handle non-contiguous\nerrors (e.g., pixel corruption). Extensive results on representative face\ndatabases (in constrained and unconstrained environments) document the\neffectiveness of our method over existing robust representation methods with\nrespect to both identification rates and computational time.\n  Code is available at Github, where you can find implementations of the\nF-LR-IRNNLS and F-IRNNLS (fast version of the RRC) :\nhttps://github.com/miliadis/FIRC","url_abs":"http://arxiv.org/abs/1605.02266v2","url_pdf":"http://arxiv.org/pdf/1605.02266v2.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":[{"paper_slug":"robust-and-low-rank-representation-for-fast","repo_url":"https://github.com/miliadis/FIRC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-identification","task_name":"Face Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}