在面部表情识别先进智能系统中利用ResNet和标签分发
Zhenggeng Qu1,2, Danying Niu3
1College of Mathematics and Computer Application, Shangluo University, Shaanxi 726000, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
本研究介绍了ResFace,这是一种面部表情识别 (FER) 的新框架,它解决了当前AI模型的局限性. ResFace 改进了特征提取,并考虑了标签分布,以更准确地检测情绪.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面部表情识别 (FER) 是计算机视觉的一个关键领域.
- 现有的FER方法经常使用单一标签,忽视标签分发问题.
- 目前的方法并不总是有效地捕捉歧视性特征.
研究的目的:
- 提出一个新的框架,ResFace,改进FER.
- 解决FER中单一标签分类的局限性.
- 为了增强对辨别特征的捕捉,以便更好地识别情绪.
主要方法:
- ResFace使用一个本地特征提取模块与ResNet-18和ResNet-50.
- 一个通道空间特征聚合模块学习高层特征.
- 一个紧的特征聚合模块使用卷积运算来学习标签分布.
主要成果:
- 在FER+数据库中,ResFace框架实现了89.87%的准确性.
- 该框架在现实世界情感面孔数据库中获得了88.38%的准确性.
- 实验结果显示性能与现有方法相比较.
结论:
- 通过结合标签分发学习,ResFace为FER提供了一种有前途的方法.
- 该框架有效地提取和汇总特征,以提高识别精度.
- 拟议的方法推进了人工智能驱动的面部表情分析的最新技术.
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