Advances in Emotional Body Gesture Recognition
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 (20 May 2022) | Viewed by 521
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,
Recognition of emotional states in the context of affective computing is a longstanding goal of many researchers. Most studies have focused on facial expressions or speech, and recently a combination of both. In many papers, the authors indicate the necessity of fusing a variety of information, which is integral to the human cognitive process.
The development of motion capture technologies such as structured-light 3D scanners, time-of-flight sensors, and more recently, Densepose mapping, has caused a surge of interest regarding the influence of gestures on emotional resonance. Gestures, as one of the most important forms of nonverbal communication, allow individuals to communicate a variety of feelings, thoughts, and emotions. However due to the limited amount of publicly accessible databases with emotional gestures as an additional mode, trimodality based recognition is still a challenging task, and there is clearly a gap to be filled regarding this kind of analysis.
This Special Issue invites contributions that address (i) systems and devices for capturing emotions based on gestures, and (ii) machine-learning techniques of relevance for tackling the issues above. In particular, submitted papers should clearly show novel contributions and innovative applications covering, but not limited to, the following topics around emotion recognition:
- Systems and devices for capturing gestures;
- Affective database creation, experimental datasets;
- Data preprocessing;
- Nonintrusive sensor technologies;
- Emotion recognition using mobile phones and smart bracelets;
- Machine-learning techniques for emotion recognition;
- Deep learning for emotion recognition.
Prof. Dr. Gholamreza Anbarjafari
Dr. Dorota Kamińska
Guest Editors
Manuscript Submission Information
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Keywords
- systems and devices for capturing gestures
- affective database creation, experimental datasets
- data preprocessing
- nonintrusive sensor technologies
- emotion recognition using mobile phones and smart bracelets
- machine-learning techniques for emotion recognition
- deep learning for emotion recognition
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