.png){kind=icon}
返回
Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data
.png){kind=icon}
OA
AI
DOI：10.1109/T-AFFC.2011.12.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The study at hand aims at the development of a multimodal, ensemble-based system for emotion recognition. Special attention is given to a problem often neglected: missing data in one or more modalities. In offline evaluation the issue can be easily solved by excluding those parts of the corpus where one or more channels are corrupted or not suitable for evaluation. In real applications, however, we cannot neglect the challenge of missing data and have to find adequate ways to handle it. To address this, we do not expect examined data to be completely available at all time in our experiments. The presented system solves the problem at the multimodal fusion stage, so various ensemble techniques-covering established ones as well as rather novel emotion specific approaches-will be explained and enriched with strategies on how to compensate for temporarily unavailable modalities. We will compare and discuss advantages and drawbacks of fusion categories and extensive evaluation of mentioned techniques is carried out on the CALLAS Expressivity Corpus, featuring facial, vocal, and gestural modalities.
Keyword:
Ensemble based systems
decision-level fusion
multimodal emotion recognition
missing data
期刊
IF:
9.8
论文数:
1.4K
被引数:
9.1K
机构
引用论文
A survey on ambient backscatter communications: Principles, systems, applications, and challenges
PREAI
分享收藏
Toward an affect-sensitive multimodal human-computer interaction走向情感敏感的多模式人机交互.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏Face detection and tracking in video sequences using the modified census transformation使用改进的census变换在视频序列中进行人脸检测和跟踪
PREAI
分享收藏
查看更多内容