|Table of Contents|

[1] Ye Jiayin, Zheng Wenming, Li Yang, Cai Youyi, et al. Multimodal emotion recognition based on deep neural network [J]. Journal of Southeast University (English Edition), 2017, 33 (4): 444-447. [doi:10.3969/j.issn.1003-7985.2017.04.009]
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Multimodal emotion recognition based on deep neural network()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
33
Issue:
2017 4
Page:
444-447
Research Field:
Image Processing
Publishing date:
2017-12-30

Info

Title:
Multimodal emotion recognition based on deep neural network
Author(s):
Ye Jiayin Zheng Wenming Li Yang Cai Youyi Cui Zhen
School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210029, China
Keywords:
emotion recognition convolutional neural network(CNN) recurrent neural networks(RNN)
PACS:
TP751
DOI:
10.3969/j.issn.1003-7985.2017.04.009
Abstract:
In order to increase the accuracy rate of emotion recognition in voice and video, the mixed convolutional neural network(CNN)and recurrent neural network(RNN)are used to encode and integrate the two information sources. For the audio signals, several frequency bands as well as some energy functions are extracted as low-level features by using a sophisticated audio technique, and then they are encoded with a one-dimensional(1D)convolutional neural network to abstract high-level features. Finally, these are fed into a recurrent neural network for the sake of capturing dynamic tone changes in a temporal dimensionality. As a contrast, a two-dimensional(2D)convolutional neural network and a similar RNN are used to capture dynamic facial appearance changes of temporal sequences. The method was used in the Chinese Natural Audio-Visual Emotion Database in the Chinese Conference on Pattern Recognition(CCPR)in 2016. Experimental results demonstrate that the classification average precision of the proposed method is 41.15%, which is increased by 16.62% compared with the baseline algorithm offered by the CCPR in 2016. It is proved that the proposed method has higher accuracy in the identification of emotional information.

References:

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Memo

Memo:
Biographies: Ye Jiayin(1993—), female, graduate; Zheng Wenming(corresponding author), male, doctor, professor, wenming_zheng@seu.edu.cn.
Citation: Ye Jiayin, Zheng Wenming, Li Yang, et al. Multimodal emotion recognition based on deep neural network[J].Journal of Southeast University(English Edition), 2017, 33(4):444-447.DOI:10.3969/j.issn.1003-7985.2017.04.009.
Last Update: 2017-12-20