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.issn |
2210-142X |
|
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 (AUs), which have many applications in Human Computer Interaction (HCI).
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 (DCGANs), 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. We demonstrate an enhanced ability to generalize and achieve successful results by using a
different methodology and a variety of datasets for feature extraction and classification. Our method has been thoroughly tested through
multiple experiments on different databases, leading to promising results. |
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 |
http://dx.doi.org/10.12785/ijcds/160193 |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1259 |
en_US |
dc.pageend |
1282 |
en_US |
dc.contributor.authorcountry |
Iraq |
en_US |
dc.contributor.authorcountry |
France |
en_US |
dc.contributor.authoraffiliation |
Department of Statistics and Informatics, College of Computer Sciences and Mathematics |
en_US |
dc.contributor.authoraffiliation |
School of Computing Science, University of Glasgow |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |