Improvement of continuous emotion recognition of temporal convolutional networks with incomplete labels

Author:

Wang Zheyu1ORCID,Zheng Jieying2,Liu Feng12

Affiliation:

1. School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

2. School of Geographic and Biologic information Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

Abstract

AbstractVideo‐based emotion recognition has been a long‐standing research topic for computer scientists and psychiatrists. In contrast to traditional discrete emotional models, emotion recognition based on continuous emotional models can better describe the progression of emotions. Quantitative analysis of emotions will have crucial impacts on promoting the development of intelligent products. The current solutions to continuous emotion recognition still have many issues. The original continuous emotion dataset contains incomplete data annotations, and the existing methods often ignore temporal information between frames. The following measures are taken in response to the above problems. Initially, aiming at the problem of incomplete video labels, the correlation between discrete and continuous video emotion labels is used to complete the dataset labels. This correlation is used to propose a mathematical model to fill the missing labels of the original dataset without adding data. Moreover, this paper proposes a continuous emotion recognition network based on an optimized temporal convolutional network, which adds a feature extraction submodule and a residual module to retain shallow features while improving the feature extraction ability. Finally, validation experiments on the Aff‐wild2 dataset achieved accuracies of 0.5159 and 0.65611 on the valence and arousal dimensions, respectively, by adopting the above measures.

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

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