Depression Estimation Using Audiovisual Features and Fisher Vector Encoding
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We investigate the use of two visual descriptors: Local Binary Patterns-Three Orthogonal Planes(LBP-TOP) and Dense Trajectories for depression assessment on the AVEC 2014 challenge dataset. We encode the visual information generated by the two descriptors using Fisher Vector encoding which has been shown to be one of the best performing methods to encode visual data for image classification. We also incorporate audio features in the final system to introduce multiple input modalities. The results produced using Linear Support Vector regression outperform the baseline method.
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Multimodality is central in depression prediction approaches, either by early fusion or late fusion with audio representations.
“Multimodality is central in such approaches either by applying early fusion [255] or late fusion [256] with audio representations.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [256]
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Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669 · see citing works
Image Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498 · see citing works
Automatic behavior descriptors for psychological disorder analysis
Scherer, S., Stratou, G., Mahmoud, M., Boberg, J., Gratch, J., Rizzo, A., & Morency, L.-P. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553789 · Cited by 147 · 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
Jan, A., Meng, H., Gaus, Y. F. B. A., & Zhang, F. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2721552 · Cited by 219 · see citing works
Fan, H., Zhang, X., Xu, Y., Fang, J., Zhang, S., Zhao, X., & Yu, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102161 · Cited by 137 · see citing works
- Authors
- Jain, V., Crowley, J. L., Dey, A. K., & Lux, A.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge
- Publisher
- ACM
- Published
- 2014-11-07
- Pages
- 87-91
- OpenAlex
- W2060779380
- Cited by
- 78
- References
- 3
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 4 · 2023: 6 · 2022: 8 · 2021: 7 · 2020: 11 · 2019: 4 · 2018: 11 · 2017: 8 · 2016: 5 · 2015: 4
- FWCI
- 9.27
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Locations
- International Workshop on Audio/Visual Emotion Challenge (AVEC) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Region Rhone-Alpes CMIRA program
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- depression detection, audio-visual features, Fisher vector encoding, local binary pattern, dense trajectories, multimodal fusion, support vector regression, facial expression analysis
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