{"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/an-efficient-method-for-face-quality","title":"An Efficient Method for Face Quality Assessment on the Edge","arxiv_id":"2207.09505","date":"2022-07-19","proceeding":null,"authors":["Sefa Burak Okcu","Burak Oğuz Özkalaycı","Cevahir Çığla"],"abstract":"Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach's efficiency through comparison with SOTA face quality regression models on different data sets and real-life scenarios.","url_abs":"https://arxiv.org/abs/2207.09505v1","url_pdf":"https://arxiv.org/pdf/2207.09505v1.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-quality-assessement","task_name":"Face Quality Assessement"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-quality-assessement-on-color-feret","task":"Face Quality Assessement","dataset":"Color FERET","model":"monet","rank_in_archive_order":1,"of":1,"metrics":{"Pearson Correlation":"0.686"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}