{"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/spontaneous-facial-micro-expression-1","title":"Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks","arxiv_id":"1904.01390","date":"2019-03-27","proceeding":null,"authors":["Sai Prasanna Teja Reddy","Surya Teja Karri","Shiv Ram Dubey","Snehasis Mukherjee"],"abstract":"Facial expression recognition in videos is an active area of research in\ncomputer vision. However, fake facial expressions are difficult to be\nrecognized even by humans. On the other hand, facial micro-expressions\ngenerally represent the actual emotion of a person, as it is a spontaneous\nreaction expressed through human face. Despite of a few attempts made for\nrecognizing micro-expressions, still the problem is far from being a solved\nproblem, which is depicted by the poor rate of accuracy shown by the\nstate-of-the-art methods. A few CNN based approaches are found in the\nliterature to recognize micro-facial expressions from still images. Whereas, a\nspontaneous micro-expression video contains multiple frames that have to be\nprocessed together to encode both spatial and temporal information. This paper\nproposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous\nfacial micro-expression recognition by exploiting the spatiotemporal\ninformation in CNN framework. The MicroExpSTCNN considers the full spatial\ninformation, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion\nof the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and\nSMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms\nthe state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1904.01390v1","url_pdf":"http://arxiv.org/pdf/1904.01390v1.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":"spontaneous-facial-micro-expression-1","repo_url":"https://github.com/bogireddytejareddy/micro-expression-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"micro-expression-recognition-1","task_name":"Micro Expression Recognition"},{"task_slug":"micro-expression-recognition","task_name":"Micro-Expression 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}