.png){kind=icon}
返回
Emotion recognition by skeleton-based spatial and temporal analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2023.121981.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study introduces an automatic emotion recognition system (AER) focusing on skeletal-based kinematic datasets for enhanced human-computer interaction. Departing from conventional approaches, it achieves realtime emotion recognition in real-life situations. The dataset covers seven emotions and undergoes assessment by eight diverse machine and deep learning algorithms. A thorough investigation is undertaken by varying window sizes and data states, including raw positions and feature-extracted data. The findings imply that incorporating advanced techniques like joint-related feature extraction and robust classifier models yields promising outcomes. Dataset augmentation via varying window sizes enriches insights into real-world scenarios. Evaluations exhibit classification accuracy surpassing 99% for small windows, 94% for medium, and exceeding 88% for larger windows, thereby confirming the robust nature of the approach. Furthermore, we highlight window size's impact on emotion detection and the benefits of combining coordinate axes for efficiency and accuracy. The analysis intricately examines the contributions of features at both the joint and axis levels, assisting in making well-informed selections. The study's contributions include carefully curated datasets, transparent code, and models, all of which ensure the possibility of replication. The paper establishes a benchmark that bridges theory and practicality, solidifying the proposed approach's effectiveness in balancing accuracy and efficiency. By pioneering advanced AER through kinematic data, it sets a new standard for efficacy while driving seamless human-computer interaction through rigorous analysis and strategic design.
Keyword:
Emotion recognition
Body posture
Deep learning
Machine learning
Feature selection
MoCap
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏A systematic review on affective computing: emotion models, databases, and recent advances情感计算的系统综述: 情感模型，数据库和最新进展.png)
INFORMATION FUSION
OAAI
INFORMATION FUSIONIF15.5
OAAI分享收藏
查看更多内容