{"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/emotion-recognition-in-talking-face-videos","title":"Emotion recognition in talking-face videos using persistent entropy and neural networks","arxiv_id":"2110.13571","date":"2021-10-26","proceeding":null,"authors":["Eduardo Paluzo-Hidalgo","Guillermo Aguirre-Carrazana","Rocio Gonzalez-Diaz"],"abstract":"The automatic recognition of a person's emotional state has become a very active research field that involves scientists specialized in different areas such as artificial intelligence, computer vision or psychology, among others. Our main objective in this work is to develop a novel approach, using persistent entropy and neural networks as main tools, to recognise and classify emotions from talking-face videos. Specifically, we combine audio-signal and image-sequence information to compute a topology signature(a 9-dimensional vector) for each video. We prove that small changes in the video produce small changes in the signature. These topological signatures are used to feed a neural network to distinguish between the following emotions: neutral, calm, happy, sad, angry, fearful, disgust, and surprised. The results reached are promising and competitive, beating the performance reached in other state-of-the-art works found in the literature.","url_abs":"https://arxiv.org/abs/2110.13571v1","url_pdf":"https://arxiv.org/pdf/2110.13571v1.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":"emotion-recognition-in-talking-face-videos","repo_url":"https://github.com/cimagroup/audiovisual-emotionrecognitionusingtda","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion 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}