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Heterogenous-view Occluded Expression Data Recognition Based on Cycle-Consistent Adversarial Network and K-SVD Dictionary Learning Under Intelligent Cooperative Robot Environment

Yu Jiang¹ and Shoulin Yin²

1. College of Fine Art and Design, Shenyang Normal University
   Shenyang, 110034 China
   zsshanln@163.com
2. Software College, Shenyang Normal University
   Shenyang, 110034 China
   yslin@synu.edu.cn

Abstract

In space art design, the recognition of expression is of great help to the understanding of art. It is very difficult to obtain occlusion expression data from robot environment. In particular, it is very challenging to recognize the occluded expression. In the case of facial occlusion, it is difficult to extract the features of occluded expressions by traditional methods. In order to reduce the dependence of expression recognition on individuals, this paper proposes a cycle-consistent adversarial network and K-SVD dictionary learning method for occluded expression recognition in education management under robot environment. Firstly, the new method uses the cyclic-consistent generation adversarial network as the skeleton model, which can generate the un-occluded expression image without the need of paired data sets. Meanwhile, in order to improve the discriminant ability and image generation ability of the network, a multi-scale discriminator is used to construct the discriminant network. Then, the least squares and cyclic sensing loss are used to strengthen the constraints on the network model and improve the image quality. By subtracting the error matrix from the test sample, a clear image of the expression classification stage can be recovered. The clear image samples are decomposed into identity features and expression features by using the collaborative representation of two dictionaries. Finally, it is classified according to the contribution of each expression feature to the joint sparse representation. Experiments conducted on CK+, RAF-DB and SFEW datasets, the results show that the average accuracy of the new model is 98.44%, 87.12% and 62.17%, respectively. Compared with the traditional convolutional neural network models and advanced methods, this model effectively improves the accuracy of facial recognition in the case of facial occlusion.

Key words

Heterogenous-view data, Intelligent Cooperative Robot, cyclic-consistent generation adversarial network, K-SVD dictionary learning

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS221228034J

Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Jiang, Y., Yin, S.: Heterogenous-view Occluded Expression Data Recognition Based on Cycle-Consistent Adversarial Network and K-SVD Dictionary Learning Under Intelligent Cooperative Robot Environment. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS221228034J