{"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/implicit-media-tagging-and-affect-prediction","title":"Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera","arxiv_id":"1701.05248","date":"2017-01-18","proceeding":null,"authors":["Daniel Hadar"],"abstract":"We present a method that automatically evaluates emotional response from spontaneous facial activity recorded by a depth camera. The automatic evaluation of emotional response, or affect, is a fascinating challenge with many applications, including human-computer interaction, media tagging and human affect prediction. Our approach in addressing this problem is based on the inferred activity of facial muscles over time, as captured by a depth camera recording an individual's facial activity. Our contribution is two-fold: First, we constructed a database of publicly available short video clips, which elicit a strong emotional response in a consistent manner across different individuals. Each video was tagged by its characteristic emotional response along 4 scales: \\emph{Valence, Arousal, Likability} and \\emph{Rewatch} (the desire to watch again). The second contribution is a two-step prediction method, based on learning, which was trained and tested using this database of tagged video clips. Our method was able to successfully predict the aforementioned 4 dimensional representation of affect, as well as to identify the period of strongest emotional response in the viewing recordings, in a method that is blind to the video clip being watch, revealing a significantly high agreement between the recordings of independent viewers.","url_abs":"http://arxiv.org/abs/1701.05248v1","url_pdf":"http://arxiv.org/pdf/1701.05248v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"implicit-media-tagging-and-affect-prediction","repo_url":"https://github.com/ednussi/3Deception","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}