Employee Emotion Recognition Method Based on Improved MobileNetV3

Xi Chen (), Miaoyun Hu (), Xinle Zou () and Yate Tan ()
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Xi Chen: Shunde Polytechnic
Miaoyun Hu: Shunde Polytechnic
Xinle Zou: Guangdong Xi’an Jiaotong University Research Institute
Yate Tan: Guangdong Xi’an Jiaotong University Research Institute

A chapter in Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024), 2024, pp 180-188 from Springer

Abstract: Abstract This paper has made improvements to the MobileNetV3 model, incorporating deep separable convolutions, inverted residual structures, and optimized time-consuming layer structures. Additionally, an improved attention mechanism has been proposed, utilizing a serial spatial channel attention mechanism. After multiple experiments, the improved model achieved an accuracy rate of 94.95% on the KDEF dataset, demonstrating that the enhancements have increased the accuracy of facial expression recognition.

Keywords: Employee Emotion Recognition Method; Improved MobileNetV3; facial expression recognition (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-638-3_18

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DOI: 10.2991/978-94-6463-638-3_18

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