CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video
Linlin Chao; Jianhua Tao; Minghao Yang; Ya Li
2014
Conference NameProceedings of the 4th International Workshop on Audio/Visual Emotion Challenge
Source PublicationProceedings of the 4th International Workshop on Audio/Visual Emotion Challenge
Pages11-18
Conference Date2014-11
Conference PlaceOrlando, Florida
AbstractUnderstanding nonverbal behaviors in human machine interaction is a complex and challenge task. One of the key aspects is to recognize human emotion states accurately. This paper presents our effort to the Audio/Visual Emotion Challenge (AVEC'14), whose goal is to predict the continuous values of the emotion dimensions arousal, valence and dominance at each moment in time. The proposed method utilizes deep belief network based models to recognize emotion states from audio and visual modalities. Firstly, we employ temporal pooling functions in the deep neutral network to encode dynamic information in the features, which achieves the first time scale temporal modeling. Secondly, we combine the predicted results from different modalities and emotion temporal context information simultaneously. The proposed multimodal-temporal fusion achieves temporal modeling for the emotion states in the second time scale.  Experiments results show the efficiency of each key point of the proposed method and competitive results are obtained.
KeywordAffective Computing
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11846
Collection模式识别国家重点实验室_语音交互
Corresponding AuthorLinlin Chao
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Linlin Chao,Jianhua Tao,Minghao Yang,et al. Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video[C],2014:11-18.
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