{"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/scut-fbp-a-benchmark-dataset-for-facial","title":"SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception","arxiv_id":"1511.02459","date":"2015-11-08","proceeding":null,"authors":["Duorui Xie","Lingyu Liang","Lianwen Jin","Jie Xu","Mengru Li"],"abstract":"In this paper, a novel face dataset with attractiveness ratings, namely, the\nSCUT-FBP dataset, is developed for automatic facial beauty perception. This\ndataset provides a benchmark to evaluate the performance of different methods\nfor facial attractiveness prediction, including the state-of-the-art deep\nlearning method. The SCUT-FBP dataset contains face portraits of 500 Asian\nfemale subjects with attractiveness ratings, all of which have been verified in\nterms of rating distribution, standard deviation, consistency, and\nself-consistency. Benchmark evaluations for facial attractiveness prediction\nwere performed with different combinations of facial geometrical features and\ntexture features using classical statistical learning methods and the deep\nlearning method. The best Pearson correlation (0.8187) was achieved by the CNN\nmodel. Thus, the results of our experiments indicate that the SCUT-FBP dataset\nprovides a reliable benchmark for facial beauty perception.","url_abs":"http://arxiv.org/abs/1511.02459v1","url_pdf":"http://arxiv.org/pdf/1511.02459v1.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":"scut-fbp-a-benchmark-dataset-for-facial","repo_url":"https://github.com/trungson077/beauty_eva","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1511.02459","atlas_url":"https://app.syntology.ai/?focus=1511.02459","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}