{"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/bi-modal-first-impressions-recognition-using","title":"Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features","arxiv_id":"1610.10048","date":"2016-10-31","proceeding":null,"authors":["Arulkumar Subramaniam","Vismay Patel","Ashish Mishra","Prashanth Balasubramanian","Anurag Mittal"],"abstract":"We propose a novel approach for First Impressions Recognition in terms of the\nBig Five personality-traits from short videos. The Big Five personality traits\nis a model to describe human personality using five broad categories:\nExtraversion, Agreeableness, Conscientiousness, Neuroticism and Openness. We\ntrain two bi-modal end-to-end deep neural network architectures using\ntemporally ordered audio and novel stochastic visual features from few frames,\nwithout over-fitting. We empirically show that the trained models perform\nexceptionally well, even after training from a small sub-portions of inputs.\nOur method is evaluated in ChaLearn LAP 2016 Apparent Personality Analysis\n(APA) competition using ChaLearn LAP APA2016 dataset and achieved excellent\nperformance.","url_abs":"http://arxiv.org/abs/1610.10048v1","url_pdf":"http://arxiv.org/pdf/1610.10048v1.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":"bi-modal-first-impressions-recognition-using","repo_url":"https://github.com/InnovArul/first-impressions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"torch","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}