Τίτλος:
Methods for Facial Expression Recognition with Applications in Challenging Situations
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
In the last few years, a great deal of interesting research has been achieved on automatic facial emotion recognition (FER). FER has been used in a number of ways to make human-machine interactions better, including human center computing and the new trends of emotional artificial intelligence (EAI). Researchers in the EAI field aim to make computers better at predicting and analyzing the facial expressions and behavior of human under different scenarios and cases. Deep learning has had the greatest influence on such a field since neural networks have evolved significantly in recent years, and accordingly, different architectures are being developed to solve more and more difficult problems. This article will address the latest advances in computational intelligence-related automated emotion recognition using recent deep learning models. We show that both deep learning-based FER and models that use architecture-related methods, such as databases, can collaborate well in delivering highly accurate results. © 2022 Anil Audumbar Pise et al.
Συγγραφείς:
Pise, A.A.
Alqahtani, M.A.
Verma, P.
Purushothama, K.
Karras, D.A.
Prathibha, S.
Halifa, A.
Περιοδικό:
Computational Intelligence and Neuroscience
Λέξεις-κλειδιά:
Behavioral research; Deep learning; Face recognition; Network architecture, Emotion models; Emotion recognition; Facial behaviours; Facial emotions; Facial expression recognition; Facial Expressions; Human machine interaction; Learning models; Neural-networks; Predicting and analyzing, Speech recognition, artificial intelligence; emotion; facial expression; facial recognition; human, Artificial Intelligence; Emotions; Facial Expression; Facial Recognition; Humans; Neural Networks, Computer
DOI:
10.1155/2022/9261438
