Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification
Open accessAbstract
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database.
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More advanced face frontalization involving approximation of 3D shape is useful when dealing with 3D head pose variation.
“More advanced face frontalization involving the approximation of 3D shape is useful when dealing with 3D head pose variation [104], [105].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [105]
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- Authors
- Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X.
- Affiliations
- Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain
- Universitat Autònoma de Barcelona (ES)
- Aalborg University (DK)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2022-05
- Volume
- 52
- Issue
- 5
- Pages
- 3314-3324
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2587128043
- Cited by
- 297
- References
- 69
- Citations by year
- 2026: 12 · 2025: 28 · 2024: 33 · 2023: 32 · 2022: 26 · 2021: 43 · 2020: 46 · 2019: 42 · 2018: 28 · 2017: 6
- FWCI
- 43.48
- Type
- journal article
- Language
- en
- Open access
- green
- vbn.aau.dk
- Licence
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, other-oa)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, public-domain)
- Funders
- Spanish Project (TIN2015-65464-R)
- Generalitat de Catalunya (2016FI_B 01163)
- COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language) through COST
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- pain assessment, facial expression recognition, long short-term memory networks, deep pain, video sequences, temporal modeling, class imbalance
- MeSH terms
- Emotions, Facial Expression, Humans, Memory, Short-Term, Pain, Neural Networks, Computer
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