Emotion Recognition in Human–Computer Interaction
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1077
Special Issue Editors
Interests: machine learning; computer vision; human–computer interaction; emotion recognition; deep learning; human behaviour analysis
Special Issues, Collections and Topics in MDPI journals
Interests: human behavior analysis; affective computing; universal design
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Human–computer interaction is increasingly utilized in smart homes, industry 4.0 and personal health. Emotion recognition connects several disciplines, i.e., psychology, electronics/sensors, signal processing, and machine learning. Many techniques have been utilized to extract emotions from signals, including deep learning and speech analysis and so on. In human–computer interaction (HCI) applications, it is essential for the machine, such as a computer or a robot, to measure, understand, simulate, and react to human emotions. Thus, emotion recognition is an important component for human–computer interaction (HCI).
We invite original research papers and review articles on human–computer interaction (HCI)-related emotion recognition innovations, including but not limited to the following topics around emotion recognition:
- Emotion recognition;
- Speech emotion recognition;
- Affective database creation and experimental datasets;
- Data preprocessing;
- Non-intrusive sensor technologies;
- Emotion recognition using mobile phones and smart bracelets;
- Machine-learning techniques for emotion recognition;
- Deep learning for emotion recognition;
- Emotion recognition in smart homes;
- Emotion recognition in industry 4.0;
- Emotion recognition using physiological signals
Prof. Dr. Gholamreza Anbarjafari
Dr. Dorota Kamińska
Guest Editors
Manuscript Submission Information
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Keywords
- emotion recognition
- speech emotion recognition
- affective database creation and experimental datasets
- data preprocessing
- non-intrusive sensor technologies
- emotion recognition using mobile phones and smart bracelets
- machine-learning techniques for emotion recognition
- deep learning for emotion recognition
- emotion recognition in smart homes
- emotion recognition in industry 4.0
- emotion recognition using physiological signals
