CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
Long Short Term Memory Recurrent Neural Network based Encoding Method for Emotion Recognition in Video
Linlin Chao; Jianhua Tao; Minghao Yang; Ya Li; Zhengqi Wen
2016
Conference NameIEEE International Conference on Acoustic, Speech and Signal Processing(ICASSP)
Source PublicationICASSP2016
Conference Date2016-3
Conference PlaceShanghai, China
AbstractHuman emotion is a temporally dynamic event which can be inferred from both audio and video feature sequences. In this paper we investigate the long short term memory recurrent neural network (LSTM-RNN) based encoding method for category emotion recognition in the video. LSTM-RNN is able to incorporate knowledge about how emotion evolves over long range successive frames and emotion clues from isolated frame. After encoding, each video clip can be represented by a vector for each input feature sequence. The vectors contain both frame level and sequence level emotion information. These vectors are then concatenated and fed into support vector machine (SVM) to get the final prediction result. Extensive evaluations on Emotion Challenge in the Wild (EmotiW2015) dataset show the efficiency of the proposed encoding method and competitive results are obtained.  The final recognition accuracy achieves 46.38% for audio-video emotion recognition sub-challenge, where the challenge baseline is 39.33%.
KeywordEmotion Recognition
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11843
Collection模式识别国家重点实验室_语音交互
Corresponding AuthorLinlin Chao
Affiliation中科院自动化研究所
Recommended Citation
GB/T 7714
Linlin Chao,Jianhua Tao,Minghao Yang,et al. Long Short Term Memory Recurrent Neural Network based Encoding Method for Emotion Recognition in Video[C],2016.
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