Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild
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In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under real-world environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, and partial least squares, are investigated for comparisons. Finally, an optimal fusion of classifiers learned from different kernels and different modalities (video and audio) is conducted at the decision level for further boosting the performance. We perform an extensive evaluation on the challenge data (including validation set and blind test set), and evaluate the effects of different strategies in our pipeline. The final recognition accuracy achieved 50.4% on test set, with a significant gain of 16.7% above the challenge baseline 33.7%.
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Recently successful methods in naturalistic environments learn representations from sequences of frames.
“Recently successful methods learn representations [108], [146], [147], [152] from sequences of frames.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [108]
While approaches to facial expression detection in naturalistic environments using static representations exist, dynamic representations are dominant.
“While approaches to FE detection in naturalistic environments using static representations exist [194], [262], dynamic representations are dominant [108], [113], [146], [147], [263], [264].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [108]
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- Authors
- Liu, M., Wang, R., Li, S., Shan, S., Huang, Z., & Chen, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- Proceedings of the 16th International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2014-11-12
- Pages
- 494-501
- OpenAlex
- W2008887256
- Cited by
- 202
- References
- 3
- Citations by year
- 2025: 3 · 2024: 7 · 2023: 5 · 2022: 25 · 2021: 20 · 2020: 18 · 2019: 27 · 2018: 21 · 2017: 22 · 2016: 34 · 2015: 19 · 2014: 1
- FWCI
- 41.45
- Type
- conference paper
- Language
- en
- Open access
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- Funders
- National Natural Science Foundation of China (61379083, 61222211, 61025010, 61390511)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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 › Image Retrieval and Classification Techniques
- Keywords
- Riemannian manifold learning, multiple kernel learning, video-based emotion recognition, audiovisual integration, decision fusion
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