Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition
Open accessAbstract
Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal facial images for pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames, improving by more than 6% the results that only consider RGB data.
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Pain detection is used for monitoring patient progress in clinical settings.
“Pain detection is used for monitoring patient progress in clinical settings [48], [49], [50].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [50]
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Cites
Prkachin, K. M., & Solomon, P. E. (2008) · Pain · doi:10.1016/j.pain.2008.04.010 · Cited by 472 · see citing works
The painful face – Pain expression recognition using active appearance models
Ashraf, A. B., Lucey, S., Cohn, J. F., Chen, T., Ambadar, Z., Prkachin, K. M., & Solomon, P. E. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.05.007 · Cited by 355 · see citing works
Continuous Pain Intensity Estimation from Facial Expressions
Kaltwang, S., Rudovic, O., & Pantic, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33191-6_36 · Cited by 245 · see citing works
Automatic coding of facial expressions displayed during posed and genuine pain
Littlewort, G. C., Bartlett, M. S., & Lee, K. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.12.010 · Cited by 216 · see citing works
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Valstar, M. F., Pantic, M., Ambadar, Z., & Cohn, J. F. (2006) · Proceedings of the 8th international conference on Multimodal interfaces · doi:10.1145/1180995.1181031 · Cited by 168 · see citing works
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Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · see citing works
Self-citationDeep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification
Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2662199 · Cited by 297 · see citing works
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- Authors
- Irani, R., Nasrollahi, K., Simon, M. O., Corneanu, C. A., Escalera, S., Bahnsen, C., Lundtoft, D. H., Moeslund, T. B., Pedersen, T. L., Klitgaard, M.-L., & Petrini, L.
- Affiliations
- Aalborg University (DK)
- Universitat Autònoma de Barcelona (ES)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 88-95
- OpenAlex
- W1938551397
- Cited by
- 42
- References
- 5
- Citations by year
- 2025: 2 · 2024: 8 · 2023: 6 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 5 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 1
- FWCI
- 5.34
- Type
- conference paper
- Language
- en
- Open access
- green
- vbn.aau.dk
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Keywords
- depth images, thermal images, spatio-temporal filtering, older adults
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