{"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/employing-fusion-of-learned-and-handcrafted","title":"Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition","arxiv_id":"1710.07662","date":"2017-10-20","proceeding":null,"authors":["Earnest E. Hansley","Mauricio Pamplona Segundo","Sudeep Sarkar"],"abstract":"We present an unconstrained ear recognition framework that outperforms\nstate-of-the-art systems in different publicly available image databases. To\nthis end, we developed CNN-based solutions for ear normalization and\ndescription, we used well-known handcrafted descriptors, and we fused learned\nand handcrafted features to improve recognition. We designed a two-stage\nlandmark detector that successfully worked under untrained scenarios. We used\nthe results generated to perform a geometric image normalization that boosted\nthe performance of all evaluated descriptors. Our CNN descriptor outperformed\nother CNN-based works in the literature, specially in more difficult scenarios.\nThe fusion of learned and handcrafted matchers appears to be complementary as\nit achieved the best performance in all experiments. The obtained results\noutperformed all other reported results for the UERC challenge, which contains\nthe most difficult database nowadays.","url_abs":"http://arxiv.org/abs/1710.07662v1","url_pdf":"http://arxiv.org/pdf/1710.07662v1.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":"employing-fusion-of-learned-and-handcrafted","repo_url":"https://github.com/maups/ear-recognition","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"}],"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}