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Deep Learning Method of Facial Expression Recognition Based on Gabor Filter Bank Combined with PCNN

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A Correction to this article was published on 01 February 2024

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Abstract

Traditional recognition methods are simple to extract features and need to be manually extracted with high complexity and unstable accuracy. The expression recognition method of deep learning still has the problems of poor network representation ability and low recognition rate. In order to fully represent the complex texture and edge features of expression images, a deep learning method of expression recognition based on Gabor representation combined with PCNN was proposed. Firstly, different Gabor representations are obtained through a set of Gabor filter banks with different proportions and directions, and the corresponding convolutional neural network model is trained to generate G-CNNs. Then, the Pulse Coupled Neural Network (PCNN) was introduced to fuse the different outputs of G-CNNs. Experiments in CK+ and JAFFE databases show that the average recognition rates of this method obtained 94.87% and 96.91%, time is 2097 ms and 6142 ms. Compared with other methods, the experimental results verify the effectiveness and superiority of the proposed method. The proposed method improves the recognition rate on the premise of ensuring the recognition efficiency.

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Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

Special thanks to the following funds for their support: Key Research Project of Natural Science in Universities of Anhui Province(No.KJ2020A0782); Provincial quality engineering in Anhui Province Grass-roots teaching and research office demonstration project(No.2018jyssf111); Data Science and Big Data Technology University First-Class Undergraduate Program Construction Center (No. 2020ylzyx02);University-level Quality Engineering Demonstration Experiment and Training Center "Big Data Comprehensive Experiment and Training Center" (No. 2020 sysxx01); 2020 Anhui Provincial College Student Innovation Plan Project (No. 202012216083).

Funding

Special thanks to the following funds for their support: Key Research Project of Natural Science in Universities of Anhui Province(No.KJ2020A0782); Provincial quality engineering in Anhui Province Grass-roots teaching and research office demonstration project(No.2018jyssf111); Data Science and Big Data Technology University First-Class Undergraduate Program Construction Center (No. 2020ylzyx02).

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Authors and Affiliations

  1. School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, Anhui, China

    Lisha Yao

  2. School of Computer Science and Technology, Anhui University, Hefei, Anhui, China

    Haifeng Zhao

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  1. Lisha Yao
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  2. Haifeng Zhao
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Contributions

Conceptualization, LY and HZ; methodology, LY and HZ; software, LY; validation, LY; formal analysis, LY; investigation, LY; resources, HZ; data curation, LY; writing—original draft preparation, LY; writing—review and editing, LY; visualization, LY; supervision, HZ; project administration, LY; funding acquisition, LY. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Lisha Yao.

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Yao, L., Zhao, H. Deep Learning Method of Facial Expression Recognition Based on Gabor Filter Bank Combined with PCNN. Wireless Pers Commun 131, 955–971 (2023). https://doi.org/10.1007/s11277-023-10463-8

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