The DOI matches its Crossref record; 159 of the 159 references it cites were found in a registry or an index.
Survey on Emotional Body Gesture Recognition
Open accessAbstract
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.
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Cites
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Cited by
Adaptive Multilayer Perceptual Attention Network for Facial Expression Recognition
Liu, H., Cai, H., Lin, Q., Li, X., & Xiao, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3165321 · Cited by 129
(2023) · doi:10.2196/54369 · Cited by 127
An Overview of Emotion in Artificial Intelligence
Assunção, G., Patrão, B., Castelo-Branco, M., & Menezes, P. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3159614 · Cited by 103
Multimodal emotion recognition: A comprehensive review, trends, and challenges
Ramaswamy, M. P. A., & Palaniswamy, S. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1563 · Cited by 70
Expression Recognition Method Based on a Lightweight Convolutional Neural Network
Zhao, G., Yang, H., & Yu, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964752 · Cited by 53
Metadata
- Authors
- Noroozi, F., Corneanu, C. A., Kaminska, D., Sapinski, T., Escalera, S., & Anbarjafari, G.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2021-04-01
- Volume
- 12
- Issue
- 2
- Pages
- 505-523
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2784655662
- Cited by
- 455
- References
- 159
- FWCI
- 42.57
- Type
- journal article
- Language
- en
- Open access
- green
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- Noroozi, F., Corneanu, C. A., Kaminska, D., Sapinski, T., Escalera, S., & Anbarjafari, G. (2021). Survey on Emotional Body Gesture Recognition. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2018.2874986
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