The DOI matches its Crossref record; 58 of the 61 references it cites were found in a registry or an index.
Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition
Abstract
Facial expression recognition (FER) is increasingly gaining importance in various emerging affective computing applications. In practice, achieving accurate FER is challenging due to the large amount of inter-personal variations such as expression intensity variations. In this paper, we propose a new spatio-temporal feature representation learning for FER that is robust to expression intensity variations. The proposed method utilizes representative expression-states (e.g., onset, apex and offset of expressions) which can be specified in facial sequences regardless of the expression intensity. The characteristics of facial expressions are encoded in two parts in this paper. As the first part, spatial image characteristics of the representative expression-state frames are learned via a convolutional neural network. Five objective terms are proposed to improve the expression class separability of the spatial feature representation. In the second part, temporal characteristics of the spatial feature representation in the first part are learned with a long short-term memory of the facial expression. Comprehensive experiments have been conducted on a deliberate expression dataset (MMI) and a spontaneous micro-expression dataset (CASME II). Experimental results showed that the proposed method achieved higher recognition rates in both datasets compared to the state-of-the-art methods.
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3 of the 61 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- roth, 0, A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations, Proc Medical Image Comput Comput -Assisted Intervention, 520
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Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 2,175
“In [71], more intensity states (onset, onset to apex transition, apex, apex to offset transition and offset)”
BackgroundA Brief Review of Facial Emotion Recognition Based on Visual Information
Ko, B. C. (2018) · Sensors · doi:10.3390/s18020401 · Cited by 790
“Therefore, this sub-section briefly introduces some popular databases related to FER consisting of 2D and 3D video sequences and still images:”
BackgroundA systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 713
Facial emotion recognition using deep learning: review and insights
Mellouk, W., & Handouzi, W. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.101 · Cited by 370
Zhang, S., Yang, Y., Chen, C., Zhang, X., Leng, Q., & Zhao, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121692 · Cited by 290
Metadata
- Authors
- Kim, D. H., Baddar, W. J., Jang, J., & Ro, Y. M.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2019-04-01
- Volume
- 10
- Issue
- 2
- Pages
- 223-236
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2606933083
- Cited by
- 257
- References
- 61
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 33 · 2023: 36 · 2022: 56 · 2021: 39 · 2020: 28 · 2019: 26
- FWCI
- 24.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- KAIST Institutional Repository (KAIST) (submittedVersion)
- Funders
- National Research Foundation of Korea
- Korea government (2015R1A2A2A01005724)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- facial expression recognition, multi-objective learning, long short-term memory, micro-expressions, class separability, affective computing
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