{"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/transferring-rich-deep-features-for-facial","title":"Transferring Rich Deep Features for Facial Beauty Prediction","arxiv_id":"1803.07253","date":"2018-03-20","proceeding":null,"authors":["Lu Xu","Jinhai Xiang","Xiaohui Yuan"],"abstract":"Feature extraction plays a significant part in computer vision tasks. In this\npaper, we propose a method which transfers rich deep features from a pretrained\nmodel on face verification task and feeds the features into Bayesian ridge\nregression algorithm for facial beauty prediction. We leverage the deep neural\nnetworks that extracts more abstract features from stacked layers. Through\nsimple but effective feature fusion strategy, our method achieves improved or\ncomparable performance on SCUT-FBP dataset and ECCV HotOrNot dataset. Our\nexperiments demonstrate the effectiveness of the proposed method and clarify\nthe inner interpretability of facial beauty perception.","url_abs":"http://arxiv.org/abs/1803.07253v1","url_pdf":"http://arxiv.org/pdf/1803.07253v1.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":"transferring-rich-deep-features-for-facial","repo_url":"https://github.com/lucasxlu/TransFBP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"facial-beauty-prediction","task_name":"Facial Beauty Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-beauty-prediction-on-eccv-hotornot","task":"Facial Beauty Prediction","dataset":"ECCV HotOrNot","model":"CNN features + Bayesian ridge regression","rank_in_archive_order":1,"of":1,"metrics":{"Pearson Correlation":"0.468"},"uses_additional_data":false},{"leaderboard":"/sota/facial-beauty-prediction-on-scut-fbp","task":"Facial Beauty Prediction","dataset":"SCUT-FBP","model":"CNN features + Bayesian ridge regression","rank_in_archive_order":1,"of":2,"metrics":{"MAE":"0.2595"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}