Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/102994
Title: Facial expression recognition in the wild : dataset configurations
Authors: Galea, Nathan
Seychell, Dylan
Keywords: Machine learning
Computer vision
Facial expression
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers
Citation: Galea, N., & Seychell, D. (2022). Facial Expression Recognition in the Wild: Dataset Configurations. 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), California. 216-219.
Abstract: Facial Expression Recognition (FER) in the wild has become an increasingly significant and focused area within computer vision, with many studies tackling different aspects to improve its recognition accuracy. This paper utilizes RAF-DB and AffectNet as the two leading datasets in the scene and compares the different experimental dataset configurations to state-of-theart techniques referred to as Amend Representation Module (ARM) and Self-Cure Network (SCN). The paper demonstrates how different dataset configurations should be the main focal point of improving the FER task and how there cannot be significant improvements in the FER task with a lack of a favorable dataset.
URI: https://www.um.edu.mt/library/oar/handle/123456789/102994
Appears in Collections:Scholarly Works - FacICTAI

Files in This Item:
File Description SizeFormat 
Facial_expression_recognition_in_the_wild.pdf
  Restricted Access
291.78 kBAdobe PDFView/Open Request a copy


Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.