Research on Facial Expression Recognition Method Based on Improved ConvNeXt

ABSTRACT Advanced facial expression recognition technology can significantly enhance human‐computer interaction and improve intelligent services for humans. This paper introduces a novel facial expression recognition method utilizing an enhanced ConvNeXt network. By integrating the SENET attention mechanism into the ConvNeXt block, key feature information extraction is effectively enhanced. Additionally, the incorporation of the focal loss (FL) function optimizes the classification performance of the network model. Experimental results show that the improved ConvNeXt network achieves higher accuracy compared to other deep learning models, with accuracy rates of 83.8% and 70.4% on the RAF‐DB and FER2013 datasets, respectively.

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Publication Details

Journal
IET Image Processing
Published
2025-01-01
DOI
https://doi.org/10.1049/ipr2.70118
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Facial Expression Recognition Method Based on Improved ConvNeXt

Xu Cheng, Yu Cao, Dan Chen
IET Image Processing
Emotion and Mood Recognition
article

Research on Facial Expression Recognition Method Based on Improved ConvNeXt

Xu Cheng, Yu Cao, Dan Chen
article en

Abstract

ABSTRACT Advanced facial expression recognition technology can significantly enhance human‐computer interaction and improve intelligent services for humans. This paper introduces a novel facial expression recognition method utilizing an enhanced ConvNeXt network. By integrating the SENET attention mechanism into the ConvNeXt block, key feature information extraction is effectively enhanced. Additionally, the incorporation of the focal loss (FL) function optimizes the classification performance of the network model. Experimental results show that the improved ConvNeXt network achieves higher accuracy compared to other deep learning models, with accuracy rates of 83.8% and 70.4% on the RAF‐DB and FER2013 datasets, respectively.

IET Image ProcessingVol. 19(1)
Shenyang Agricultural University (CN), Liaoning Shihua University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 88%
Emotion and Mood Recognition
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