{"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/deep-impression-audiovisual-deep-residual","title":"Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition","arxiv_id":"1609.05119","date":"2016-09-16","proceeding":null,"authors":["Yağmur Güçlütürk","Umut Güçlü","Marcel A. J. van Gerven","Rob Van Lier"],"abstract":"Here, we develop an audiovisual deep residual network for multimodal apparent\npersonality trait recognition. The network is trained end-to-end for predicting\nthe Big Five personality traits of people from their videos. That is, the\nnetwork does not require any feature engineering or visual analysis such as\nface detection, face landmark alignment or facial expression recognition.\nRecently, the network won the third place in the ChaLearn First Impressions\nChallenge with a test accuracy of 0.9109.","url_abs":"http://arxiv.org/abs/1609.05119v1","url_pdf":"http://arxiv.org/pdf/1609.05119v1.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":"deep-impression-audiovisual-deep-residual","repo_url":"https://github.com/yagguc/deep_impression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"personality-trait-recognition","task_name":"Personality Trait Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}