dc.contributor.author |
Alharbawee, Luma |
|
dc.contributor.author |
Pugeault, Nicolas |
|
dc.date.accessioned |
2024-01-09T16:41:07Z |
|
dc.date.available |
2024-01-09T16:41:07Z |
|
dc.date.issued |
2024-01-09 |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5334 |
|
dc.description.abstract |
The task of modeling and identifying people’s emotions using facial cues is a complex problem in computer vision. Normally
we approach these issues by identifying Action Units, which have many applications in Human Computer Interaction. Although Deep
Learning approaches have demonstrated a high level of performance in recognizing AUs and emotions, they require large datasets of
expert-labelled examples. In this article, we demonstrate that good deep features can be learnt in an unsupervised fashion using Deep
Convolutional Generative Adversarial Networks, allowing for a supervised classifier to be learned from a smaller labelled dataset. The
paper primarily focuses on two key aspects: firstly, the generation of facial expression images across a wide range of poses (including
frontal, multi-view, and unconstrained environments), and secondly, the analysis and classification of emotion categories and Action
Units. Utilizing a pioneering methodology and incorporating an extensive array of datasets for feature acquisition and classification, we
substantiate a remarkably persuasive generalization and achieve enhanced outcomes. In contrast to prevailing state-of-the-art techniques,
our proposed model showcases exceptional performance, specifically on the Radboud dataset, boasting an unparalleled overall accuracy
rate of 98.57%. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Affective computing; GANs; DCGAN; fine-tuning; transfer learning; relabelling; generalisation; FACS. |
en_US |
dc.title |
Generative Adversarial Networks for Facial Expression Recognition in the Wild |
en_US |
dc.identifier.doi |
10.12785/ijcds/xxxxxx |
|
dc.volume |
15 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
17 |
en_US |
dc.contributor.authorcountry |
Mosul, Iraq |
en_US |
dc.contributor.authorcountry |
Paris, France |
en_US |
dc.contributor.authoraffiliation |
College of Computer Sciences and Mathematics, Department of Statistics and Informatics, University of Mosul |
en_US |
dc.contributor.authoraffiliation |
The School of Computing at the University of Glasgow |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |