{"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/real-time-emotion-recognition-for-gaming","title":"Real-time emotion recognition for gaming using deep convolutional network features","arxiv_id":"1408.3750","date":"2014-08-16","proceeding":null,"authors":["Sébastien Ouellet"],"abstract":"The goal of the present study is to explore the application of deep\nconvolutional network features to emotion recognition. Results indicate that\nthey perform similarly to other published models at a best recognition rate of\n94.4%, and do so with a single still image rather than a video stream. An\nimplementation of an affective feedback game is also described, where a\nclassifier using these features tracks the facial expressions of a player in\nreal-time.","url_abs":"http://arxiv.org/abs/1408.3750v1","url_pdf":"http://arxiv.org/pdf/1408.3750v1.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":"real-time-emotion-recognition-for-gaming","repo_url":"https://github.com/Zebreu/ConvolutionalEmotion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}