{"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/grammatical-facial-expression-recognition","title":"Grammatical facial expression recognition using customized deep neural network architecture","arxiv_id":"1711.06303","date":"2017-11-16","proceeding":null,"authors":["Devesh Walawalkar"],"abstract":"This paper proposes to expand the visual understanding capacity of computers\nby helping it recognize human sign language more efficiently. This is carried\nout through recognition of facial expressions, which accompany the hand signs\nused in this language. This paper specially focuses on the popular Brazilian\nsign language (LIBRAS). While classifying different hand signs into their\nrespective word meanings has already seen much literature dedicated to it, the\nemotions or intention with which the words are expressed haven't primarily been\ntaken into consideration. As from our normal human experience, words expressed\nwith different emotions or mood can have completely different meanings attached\nto it. Lending computers the ability of classifying these facial expressions,\ncan help add another level of deep understanding of what the deaf person\nexactly wants to communicate. The proposed idea is implemented through a deep\nneural network having a customized architecture. This helps learning specific\npatterns in individual expressions much better as compared to a generic\napproach. With an overall accuracy of 98.04%, the implemented deep network\nperforms excellently well and thus is fit to be used in any given practical\nscenario.","url_abs":"http://arxiv.org/abs/1711.06303v1","url_pdf":"http://arxiv.org/pdf/1711.06303v1.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":"grammatical-facial-expression-recognition","repo_url":"https://github.com/rohithv/Grammatical-facial-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)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}