Video modeling and learning on Riemannian manifold for emotion recognition in the wild
文献类型：期刊论文
| 作者 | Liu, Mengyi; Wang, Ruiping; Li, Shaoxin; Huang, Zhiwu; Shan, Shiguang; Chen, Xilin |
| 刊名 | JOURNAL ON MULTIMODAL USER INTERFACES
 |
| 出版日期 | 2016-06-01 |
| 卷号 | 10期号:2页码:113-124 |
| ISSN号 | 1783-7677 |
| 关键词 | Emotion recognition Video modeling Riemannian manifold EmotiW challenge |
| DOI | 10.1007/s12193-015-0204-5 |
| 英文摘要 | In this paper, we present the method for our submission to the emotion recognition in the wild challenge (EmotiW). 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 extensive evaluations on the EmotiW 2014 challenge data (including validation set and blind test set), and evaluate the effects of different components in our pipeline. It is observed that our method has achieved the best performance reported so far. To further evaluate the generalization ability, we also perform experiments on the EmotiW 2013 data and two well-known lab-controlled databases: CK+ and MMI. The results show that the proposed framework significantly outperforms the state-of-the-art methods. |
| 资助项目 | 973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61222211] ; Natural Science Foundation of China[61379083] ; Youth Innovation Promotion Association CAS[2015085] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:000378580400003 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8306]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Shan, Shiguang |
| 作者单位 | Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Mengyi,Wang, Ruiping,Li, Shaoxin,et al. Video modeling and learning on Riemannian manifold for emotion recognition in the wild[J]. JOURNAL ON MULTIMODAL USER INTERFACES,2016,10(2):113-124. |
| APA | Liu, Mengyi,Wang, Ruiping,Li, Shaoxin,Huang, Zhiwu,Shan, Shiguang,&Chen, Xilin.(2016).Video modeling and learning on Riemannian manifold for emotion recognition in the wild.JOURNAL ON MULTIMODAL USER INTERFACES,10(2),113-124. |
| MLA | Liu, Mengyi,et al."Video modeling and learning on Riemannian manifold for emotion recognition in the wild".JOURNAL ON MULTIMODAL USER INTERFACES 10.2(2016):113-124. |
入库方式： OAI收割
来源：计算技术研究所
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