Facial Expression Recognition on Wild and Multi-Label Faces with Deep Learning
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Aksaray University;IEEE Seccion Espana;University de La Laguna
2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 -- 19 July 2023 through 21 July 2023 -- -- 192890
2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 -- 19 July 2023 through 21 July 2023 -- -- 192890
Anahtar Kelimeler
Deep Learning, Facial Expression Recognition, Multi-Label Classification, Benchmarking, Cell Proliferation, Classification (Of İnformation), Deep Learning, Learning Algorithms, Current, Deep Learning, Facial Expression Recognition, Facial Expressions, Multi-Label Classifications, Multi-Labels, Non-Verbal Behaviours, State-Of-The-Art Performance, Face Recognition
Kaynak
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
WoS Q Değeri
Scopus Q Değeri
N/A
