Dimensional emotion recognition based on ConvLSTM network with double stream convolution feature

Mei Qi; Yun-chang Shi; Zhen Zheng; Ze-fen Liu

doi:10.1117/12.2671164

28 April 2023 Dimensional emotion recognition based on ConvLSTM network with double stream convolution feature

Mei Qi, Yun-chang Shi, Zhen Zheng, Ze-fen Liu

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126103E (2023) https://doi.org/10.1117/12.2671164
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Academic emotions are closely related to the achievement of online learning goals, for the current problem that discrete emotion recognition cannot describe the continuous dimensional academic emotion changes, this paper proposes a dimensional emotion recognition method based on a dual-streams convolutional feature ConvLSTM network. Firstly, static convolution features of single frame images in video sequences are extracted, and dynamic convolution features are extracted from video sequences, secondly, the concatenated double stream convolution features are analyzed by ConvLSTM network to extract the sequence features containing spatio-temporal information; finally, the generated feature passes through two full connection layers and outputs the predicted value of Valence-Arousal. The average of the Concordance Correlation Coefficient (CCC) on the public data set AVEC2015 reached 0.198. The experiment proved that the CCC correlation coefficient index of the proposed method on the AVEC2015 data set increased by 2.64%~6.28% compared with the baseline method, which can effectively identify dimensional emotions, providing a method support for dimensional emotion recognition.

Citation Download Citation

Mei Qi, Yun-chang Shi, Zhen Zheng, and Ze-fen Liu "Dimensional emotion recognition based on ConvLSTM network with double stream convolution feature", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126103E (28 April 2023); https://doi.org/10.1117/12.2671164

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KEYWORDS

Emotion

Convolution

Feature extraction

Video

Machine learning

Education and training

Facial recognition systems

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