Advances in Emotional Body Gesture Recognition

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

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