一个细粒度的人类面部关键特征提取和融合方法用于情绪识别和认知
Shiwei Li1,2, Jisen Wang3, Linbo Tian3
1School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China. liswyunshu@126.com.
Scientific reports
|February 20, 2025
概括
这项研究引入了一种通过整合全球和本地面部特征来准确识别面部情绪的新方法. 这种方法显著提高了情绪检测的准确性,特别是当将情绪分为积极,中性和消极类别时.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 面部情绪识别对于人机交互至关重要,但受到照明,姿势和微表情变化的挑战.
- 仅依赖全球或本地面部特征的现有方法往往忽略了关键变异,导致特征注意力不均,准确性降低.
- 准确的情绪识别需要强大的特征提取和融合技术,这些技术可以处理各种面部表情和环境条件.
研究的目的:
- 通过整合全球和本地面部特征,提出和评估一种新的面部情绪识别方法.
- 通过解决现有特征提取技术的局限性,提高情绪识别模型的准确性和稳定性.
- 调查简化情绪分类 (积极,中性,负面) 对识别表现的影响.
主要方法:
- 一个全面的图像预处理管道,包括超分辨率,照明/阴影校正和纹理增强.
- 使用编码解码架构和通过 Haar 级联分类器进行本地特征提取的全球面部特征模型的开发.
- 实现一个带有自适应融合模块的双分支卷积神经网络,以整合全球和本地面部特征.
主要成果:
- 拟议的模型在FER-2013数据集上实现了平均准确率80.59%,在JAFFE数据集上达到97.61%.
- 综合方法在面部情绪识别方面表现出优越的性能,与现有的最先进模型相比.
- 将情绪分为积极,中性和消极类别的分类显著提高了识别准确性,在自建数据集上进行验证.
结论:
- 拟议的方法通过整合全球和本地数据,有效地模拟和提取关键面部特征,提高情绪识别的准确性.
- 适应融合模块在区分微妙的情绪变化方面发挥着至关重要的作用,从而带来更强大的性能.
- 简化的三类情感分类对实际应用有重大影响,并进一步提高了模型性能.
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