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InceptionNeXt Network with Relative Position Information for Microexpression Recognition

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Wireless Artificial Intelligent Computing Systems and Applications (WASA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14999))

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Abstract

Micro-expression recognition is one of the most challenging tasks in affective computing, aiming to identify subtle facial movements that are difficult for humans to perceive within a short period. In recent years, convolutional neural networks (CNNs) have been widely employed for micro-expression recognition. Despite achieving high recognition accuracy, these approaches still exhibit some limitations. Traditional CNNs learn solely from complete images, failing to accurately capture the fine details of facial micro-expressions. Traditional convolution uses small convolution kernels, which ignore global image features during feature extraction, affecting the accuracy of micro expression recognition. In response to the shortcomings of convolutional kernels, this paper proposes a large kernel convolutional neural network for micro expression recognition. Specifically: 1) Introducing a large-kernel convolutional neural network (LKCNN) that extends the model’s receptive field, enabling it to capture data relationships over a larger range. This enhances the model’s performance. To balance computational costs, the network utilizes Inception depthwise convolution, which decomposes expensive depthwise convolutions into three convolutional branches with small kernel sizes, along with an identity mapping branch. 2) The input is transformed from entire images to image blocks, followed by the incorporation of relative position attention (RPI). This not only magnifies detailed features but also filters important feature information, ensuring the full utilization of subtle information. Experiments were conducted on three publicly available databases: CASME II, SMIC, and SAMM. The results demonstrate superior performance of the proposed method in three-class and five-class recognition compared to state-of-the-art approaches.

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Acknowledgments

This work was supported in part by the National Key R&D Program of China with Grant No.2019YFB2102600, the National Natural Science Foundation of China (NSFC) under Grant 62272256, the Shandong Provincial Natural Science Foundation under Grants ZR2023MF040 and ZR2021MF026, the Innovation Capability Enhancement Program for Small and Medium-sized Technological Enterprises of Shandong Province under Grants 2022TSGC2180 and 2022TSGC2123, the Innovation Team Cultivating Program of Jinan under Grant 202228093, the Fundamental Research Enhancement Program of Computer Science and Technology in Qilu University of Technology under Grant2021JC02014, the Talent Cultivation Promotion Program of Computer Science and Technology in Qilu University of Technology under Grant 2023PY059.

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Correspondence to Anming Dong .

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Cao, Z., Dong, A., Yu, J., Li, S., Tian, X., Zhang, L. (2025). InceptionNeXt Network with Relative Position Information for Microexpression Recognition. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14999. Springer, Cham. https://doi.org/10.1007/978-3-031-71470-2_15

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  • DOI: https://doi.org/10.1007/978-3-031-71470-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71469-6

  • Online ISBN: 978-3-031-71470-2

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