基于关系感知和标签消歧的细粒度面部表情识别算法

刘雅芝; 许喆铭; 郎丛妍; 王涛; 李浥东

doi:10.12263/DZXB.20240364

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基于关系感知和标签消歧的细粒度面部表情识别算法

学术论文 | 更新时间：2026-05-07

- 基于关系感知和标签消歧的细粒度面部表情识别算法
- Fine-Grained Facial Expression Recognition Algorithm Based on Relationship-Awareness and Label Disambiguation
- 电子学报 2024年52卷第10期页码：3336-3346
- 作者机构：
  
  1.北京交通大学计算机科学与技术学院，北京 100044
  2.北京交通大学交通大数据与人工智能教育部重点实验室，北京 100044
- 作者简介：
  
  [ "刘雅芝女，硕士研究生.主要研究方向为计算机视觉和人脸表情识别.E-mail: 22120395@bjtu.edu.cn" ]
  [ "许喆铭女，博士研究生.主要研究方向为车辆重识别和多视图学习.E-mail: 21112016@bjtu.edu.cn" ]
  [ "郎丛妍女，博士.教授，博士生导师，CCF会员.主要研究方向为计算机视觉和多媒体内容分析.E-mail: cylang@bjtu.edu.cn" ]
  [ "王涛男，博士.教授.主要研究方向为计算机视觉和机器学习.E-mail: twang@bjtu.edu.cn" ]
  [ "李浥东男，博士.教授，博士生导师.主要研究方向为大数据分析、隐私保护、信息安全和数据挖掘.E-mail: ydli@bjtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62072027;62376020)
- DOI：10.12263/DZXB.20240364
  中图分类号： TP391
- 收稿：2024-04-22，
  
  修回：2024-08-11，
  
  纸质出版：2024-10-25
- 稿件说明：
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刘雅芝, 许喆铭, 郎丛妍, 等. 基于关系感知和标签消歧的细粒度面部表情识别算法[J]. 电子学报, 2024, 52(10): 3336-3346.

LIU Ya-zhi, XU Zhe-ming, LANG Cong-yan, et al. Fine-Grained Facial Expression Recognition Algorithm Based on Relationship-Awareness and Label Disambiguation[J]. Acta Electronica Sinica, 2024, 52(10): 3336-3346.
刘雅芝, 许喆铭, 郎丛妍, 等. 基于关系感知和标签消歧的细粒度面部表情识别算法[J]. 电子学报, 2024, 52(10): 3336-3346. DOI：10.12263/DZXB.20240364

LIU Ya-zhi, XU Zhe-ming, LANG Cong-yan, et al. Fine-Grained Facial Expression Recognition Algorithm Based on Relationship-Awareness and Label Disambiguation[J]. Acta Electronica Sinica, 2024, 52(10): 3336-3346. DOI：10.12263/DZXB.20240364

摘要

细粒度表情识别任务因其包含更丰富真实的人类情感而备受关注.现有面部表情识别算法通过提取局部关键区域等方式学习更优的图像表征.然而，这些方法忽略了图像数据集内在的结构关系，且没有充分利用标签间的语义关联度以及图像和标签间的相关性，导致所学特征带来的性能提升有限.其次，现有细粒度表情识别方法并未有效利用和挖掘粗细粒度的层级关系，因而限制了模型的识别性能.此外，现有细粒度表情识别算法忽略了由于标注主观性和情感复杂性导致的标签歧义性问题，极大影响了模型的识别性能.针对上述问题，本文提出一种基于关系感知和标签消歧的细粒度面部表情识别算法（fine-grained facial expression recognition algorithm based on Relationship-Awareness and Label Disambiguation，RALD）.该算法通过构建层级感知的图像特征增强网络，充分挖掘图像之间、层级标签之间以及图像和标签之间的依赖关系，以获得更具辨别性的图像特征.针对标签歧义性问题，算法设计了基于近邻样本的标签分布学习模块，通过整合邻域信息进行标签消歧，进一步提升模型识别性能.在细粒度表情识别数据集FG-Emotions上算法的准确度达到97.34%，在粗粒度表情识别数据集RAF-DB上比现有主流表情分类方法提高了0.80%～4.55%.

Abstract

There has been a growing interest in fine-grained facial expression recognition due to its ability to capture more subtle and realistic human emotions. Existing facial expression recognition algorithms enhance image representations by extracting local key regions and other relevant features. However

these methods disregard the inherent structural relationships within the image dataset and fail to fully exploit the semantic correlation between labels and the relationship between images and labels

which restricts the enhancement of feature learning. Besides

current fine-grained expression recognition methods do not effectively explore and utilize the hierarchical relationship between coarse and fine-grained levels

which limits the recognition performance of the model. In addition

existing fine-grained expression recognition algorithms ignore the label ambiguity problem caused by labeling subjectivity and emotional complexity

which greatly affects the recognition performance of the model. To address these issues

we propose a fine-grained facial expression recognition algorithm based on relationship-awareness and label disambiguation (RALD). This algorithm enhances image features by constructing a hierarchy-aware image feature enhancement network

thoroughly exploring the dependencies among images

hierarchical labels

and between images and labels to obtain more discriminative image features. As for the issue of label ambiguity

this algorithm designs a nearest neighbors-based label distribution learning module

which further improves recognition performance by integrating neighborhood information for label disambiguation. Our algorithm achieves 97.34% in terms of accuracy on the FG-Emotions dataset for fine-grained expression recognition. Additionally

it outperforms existing mainstream facial expression recognition algorithms by 0.80% to 4.55% on the RAF-DB dataset for coarse-grained expression recognition.

关键词

Keywords

references

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