Survey on Emotional Body Gesture Recognition
By Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kaminska, Tomasz Sapinski, Sergio Escalera, Gholamreza Anbarjafari. IEEE Transactions on Affective Computing, 2018
412 citations recorded by the source corpus.
Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as ”body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g., human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce. There is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/41367bca49675d0dd078dcd9a140b92d05379900
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Computer Vision and Pattern Recognition
Citations in the corpus
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “The classification for EBGR utilizes the ML-based classifiers such as decision trees, ensemble decision trees, KNN, SVM and neural network classifiers.”
- Deep 3D human pose estimation: A review (2021, neutral): “3D human body poses can also reveal the mental states of people and the emotion can even be recognized from poses (Noroozi et al., 2018).”