{"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-fbp5500-a-diverse-benchmark-dataset-for","title":"SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction","arxiv_id":"1801.06345","date":"2018-01-19","proceeding":null,"authors":["Lingyu Liang","Luojun Lin","Lianwen Jin","Duorui Xie","Mengru Li"],"abstract":"Facial beauty prediction (FBP) is a significant visual recognition problem to\nmake assessment of facial attractiveness that is consistent to human\nperception. To tackle this problem, various data-driven models, especially\nstate-of-the-art deep learning techniques, were introduced, and benchmark\ndataset become one of the essential elements to achieve FBP. Previous works\nhave formulated the recognition of facial beauty as a specific supervised\nlearning problem of classification, regression or ranking, which indicates that\nFBP is intrinsically a computation problem with multiple paradigms. However,\nmost of FBP benchmark datasets were built under specific computation\nconstrains, which limits the performance and flexibility of the computational\nmodel trained on the dataset. In this paper, we argue that FBP is a\nmulti-paradigm computation problem, and propose a new diverse benchmark\ndataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty\nprediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with\ndiverse properties (male/female, Asian/Caucasian, ages) and diverse labels\n(face landmarks, beauty scores within [1,~5], beauty score distribution), which\nallows different computational models with different FBP paradigms, such as\nappearance-based/shape-based facial beauty classification/regression model for\nmale/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP\nusing different combinations of feature and predictor, and various deep\nlearning methods. The results indicates the improvement of FBP and the\npotential applications based on the SCUT-FBP5500.","url_abs":"http://arxiv.org/abs/1801.06345v1","url_pdf":"http://arxiv.org/pdf/1801.06345v1.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-fbp5500-a-diverse-benchmark-dataset-for","repo_url":"https://github.com/HCIILAB/SCUT-FBP5500-Database-Release","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"scut-fbp5500-a-diverse-benchmark-dataset-for","repo_url":"https://github.com/BFD91/facial-beauty-rating-tinder-bot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"scut-fbp5500-a-diverse-benchmark-dataset-for","repo_url":"https://github.com/lucasxlu/ComboLoss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"scut-fbp5500-a-diverse-benchmark-dataset-for","repo_url":"https://github.com/lucasxlu/TransFBP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"scut-fbp5500-a-diverse-benchmark-dataset-for","repo_url":"https://github.com/ptran1203/facial_beauty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"facial-beauty-prediction","task_name":"Facial Beauty Prediction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-beauty-prediction-on-scut-fbp","task":"Facial Beauty Prediction","dataset":"SCUT-FBP","model":"Combined Features + Gaussian Reg","rank_in_archive_order":2,"of":2,"metrics":{"MAE":"0.3931"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}