Long Short Term Memory Recurrent Neural Network based Encoding Method for Emotion Recognition in Video

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	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
会议日期	2016-3
会议地点	Shanghai, China
关键词	Emotion Recognition
英文摘要	Human 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%.
会议录	ICASSP2016
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/11843]
专题	自动化研究所_模式识别国家重点实验室_人机语音交互团队
通讯作者	Linlin Chao
作者单位	中科院自动化研究所
推荐引用方式 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]. 见:. Shanghai, China. 2016-3.