{"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/survey-on-emotional-body-gesture-recognition","title":"Survey on Emotional Body Gesture Recognition","arxiv_id":"1801.07481","date":"2018-01-23","proceeding":null,"authors":["Fatemeh Noroozi","Ciprian Adrian Corneanu","Dorota Kamińska","Tomasz Sapiński","Sergio Escalera","Gholamreza Anbarjafari"],"abstract":"Automatic emotion recognition has become a trending research topic in the\npast decade. While works based on facial expressions or speech abound,\nrecognizing affect from body gestures remains a less explored topic. We present\na new comprehensive survey hoping to boost research in the field. We first\nintroduce emotional body gestures as a component of what is commonly known as\n\"body language\" and comment general aspects as gender differences and culture\ndependence. We then define a complete framework for automatic emotional body\ngesture recognition. We introduce person detection and comment static and\ndynamic body pose estimation methods both in RGB and 3D. We then comment the\nrecent literature related to representation learning and emotion recognition\nfrom images of emotionally expressive gestures. We also discuss multi-modal\napproaches that combine speech or face with body gestures for improved emotion\nrecognition. While pre-processing methodologies (e.g. human detection and pose\nestimation) are nowadays mature technologies fully developed for robust large\nscale analysis, we show that for emotion recognition the quantity of labelled\ndata is scarce, there is no agreement on clearly defined output spaces and the\nrepresentations are shallow and largely based on naive geometrical\nrepresentations.","url_abs":"http://arxiv.org/abs/1801.07481v1","url_pdf":"http://arxiv.org/pdf/1801.07481v1.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":"survey-on-emotional-body-gesture-recognition","repo_url":"https://github.com/mikecheninoulu/Emotional-gesture-papers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"human-detection","task_name":"Human Detection"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1801.07481","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}