{"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/regularizing-face-verification-nets-for-pain","title":"Regularizing Face Verification Nets For Pain Intensity Regression","arxiv_id":"1702.06925","date":"2017-02-22","proceeding":null,"authors":["Feng Wang","Xiang Xiang","Chang Liu","Trac. D. Tran","Austin Reiter","Gregory D. Hager","Harry Quon","Jian Cheng","Alan L. Yuille"],"abstract":"Limited labeled data are available for the research of estimating facial\nexpression intensities. For instance, the ability to train deep networks for\nautomated pain assessment is limited by small datasets with labels of\npatient-reported pain intensities. Fortunately, fine-tuning from a\ndata-extensive pre-trained domain, such as face verification, can alleviate\nthis problem. In this paper, we propose a network that fine-tunes a\nstate-of-the-art face verification network using a regularized regression loss\nand additional data with expression labels. In this way, the expression\nintensity regression task can benefit from the rich feature representations\ntrained on a huge amount of data for face verification. The proposed\nregularized deep regressor is applied to estimate the pain expression intensity\nand verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving\nthe state-of-the-art performance. A weighted evaluation metric is also proposed\nto address the imbalance issue of different pain intensities.","url_abs":"http://arxiv.org/abs/1702.06925v3","url_pdf":"http://arxiv.org/pdf/1702.06925v3.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":"regularizing-face-verification-nets-for-pain","repo_url":"https://github.com/happynear/PainRegression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"regularizing-face-verification-nets-for-pain","repo_url":"https://github.com/Dijaq/pain_regression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"pain-intensity-regression","task_name":"Pain Intensity Regression"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pain-intensity-regression-on-unbc-mcmaster","task":"Pain Intensity Regression","dataset":"UNBC-McMaster ShoulderPain dataset","model":"Regularized Deep Regressor","rank_in_archive_order":1,"of":3,"metrics":{"MAE":"0.389"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}