Facial Expression Recognition in the Wild: Dataset Configurations

Nathan Galea, D. Seychell
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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.
野外面部表情识别:数据集配置
面部表情识别(FER)已成为计算机视觉领域中一个越来越重要和关注的领域,许多研究从不同的方面来提高其识别精度。本文利用RAF-DB和AffectNet作为场景中的两个主要数据集,并将不同的实验数据集配置与称为修正表示模块(ARM)和自我修复网络(SCN)的最新技术进行了比较。本文论证了不同的数据集配置如何成为改进FER任务的主要焦点,以及缺乏有利的数据集如何无法显著改进FER任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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