Robust facial expression recognition system in higher poses

Simple item page
dc.contributor.authorOwusu, E.
dc.contributor.authorAppati, J.K.
dc.contributor.authorOkae, P.
dc.date.accessioned2024-08-22T08:50:11Z
dc.date.available2024-08-22T08:50:11Z
dc.date.issued2022
dc.descriptionResearch Articleen_US
dc.description.abstractFacial expression recognition (FER) has numerous applications in computer security, neuroscience, psychology, and engineering. Owing to its non-intrusiveness, it is considered a useful technology for combating crime. However, FER is plagued with several challenges, the most serious of which is its poor prediction accuracy in severe head poses. The aim of this study, therefore, is to improve recognition accuracy in severe head poses by proposing a robust 3D head-tracking algorithm based on an ellipsoidal model, advanced ensemble of AdaBoost, and saturated vector machine (SVM). The FER features are tracked from one frame to the next using the ellipsoidal tracking model, and the Visible, expressive facial key points are extracted using Gabor filters. The ensemble algorithm (Ada-AdaSVM) is then used for feature selection and classification. The proposed technique is evaluated using the Bosphorus, BU-3DFE, MMI, CK+ and BP4D-Spontaneous facial expression databases. The overall performance is outstanding.en_US
dc.identifier.otherhttps://doi.org/10.1186/s42492-022-00109-0
dc.identifier.urihttps://ugspace.ug.edu.gh/handle/123456789/42356
dc.language.isoenen_US
dc.publisherVisual Computing for Industry, Biomedicine, and Arten_US
dc.subjectFacial expressionsen_US
dc.subjectThree-dimensional head poseen_US
dc.subjectEllipsoidal modelen_US
dc.titleRobust facial expression recognition system in higher posesen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Robust facial expression recognition system in higher poses.pdf
Size:
4.83 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Department of Computer Science