Facial Expression Recognition on Wild and Multi-Label Faces with Deep Learning
dc.authorscopusid | 57311290500 | |
dc.authorscopusid | 36782998200 | |
dc.contributor.author | Han, H. | |
dc.contributor.author | Sonmez, E.B. | |
dc.date.accessioned | 2024-07-18T20:17:05Z | |
dc.date.available | 2024-07-18T20:17:05Z | |
dc.date.issued | 2023 | |
dc.description | Aksaray University;IEEE Seccion Espana;University de La Laguna | en_US |
dc.description | 2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 -- 19 July 2023 through 21 July 2023 -- -- 192890 | en_US |
dc.description.abstract | The analysis of facial expressions is a powerful tool to decode nonverbal behavior in humans. Due to its importance, several studies have already been done in the past. However, facial expression recognition on wild and multi-label faces is under-investigated also due to the limited number of available databases. This paper fills in the current lack by challenging the RAF-ML dataset and fixing the state-of-the-art performance of 50.5% on the "single label experiment". The proposed method is also tested in a second experiment, suggested by this work, which considers only wild faces having a dominant expression. The benchmark performance for the second trial is 56.1%. The deep-learning algorithms presented in this work are described in detail to facilitate their reproduction. © 2023 IEEE. | en_US |
dc.identifier.doi | 10.1109/ICECCME57830.2023.10253453 | |
dc.identifier.isbn | 9798350322972 | |
dc.identifier.scopus | 2-s2.0-85174021447 | en_US |
dc.identifier.scopusquality | N/A | en_US |
dc.identifier.uri | https://doi.org/10.1109/ICECCME57830.2023.10253453 | |
dc.identifier.uri | https://hdl.handle.net/11411/6406 | |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Facial Expression Recognition | en_US |
dc.subject | Multi-Label Classification | en_US |
dc.subject | Benchmarking | en_US |
dc.subject | Cell Proliferation | en_US |
dc.subject | Classification (Of İnformation) | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Learning Algorithms | en_US |
dc.subject | Current | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Facial Expression Recognition | en_US |
dc.subject | Facial Expressions | en_US |
dc.subject | Multi-Label Classifications | en_US |
dc.subject | Multi-Labels | en_US |
dc.subject | Non-Verbal Behaviours | en_US |
dc.subject | State-Of-The-Art Performance | en_US |
dc.subject | Face Recognition | en_US |
dc.title | Facial Expression Recognition on Wild and Multi-Label Faces with Deep Learning | |
dc.type | Conference Object |