Research on Facial Expression Recognition Method Based on Improved ConvNeXt

IF 2 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IET Image Processing Pub Date : 2025-05-30 DOI:10.1049/ipr2.70118

Dan Chen, Yu Cao, Xu Cheng

引用次数: 0

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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基于改进卷积神经网络的面部表情识别方法研究

先进的面部表情识别技术可以显著增强人机交互，为人类提供智能服务。介绍了一种基于增强卷积神经网络的面部表情识别方法。通过将SENET注意机制集成到ConvNeXt块中，有效增强了关键特征信息的提取。此外，引入焦点损失（focal loss， FL）函数优化了网络模型的分类性能。实验结果表明，与其他深度学习模型相比，改进的ConvNeXt网络具有更高的准确率，在RAF-DB和FER2013数据集上的准确率分别为83.8%和70.4%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IET Image Processing 工程技术-工程：电子与电气

CiteScore

5.40

自引率

8.70%

发文量

282

审稿时长

6 months

期刊介绍： The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications. Principal topics include: Generation and Display - Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality. Processing and Analysis - Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing. Implementations and Architectures - Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing. Coding and Transmission - Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video. Retrieval and Multimedia - Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography. Applications - Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security. Current Special Issue Call for Papers: Evolutionary Computation for Image Processing - https://digital-library.theiet.org/files/IET_IPR_CFP_EC.pdf AI-Powered 3D Vision - https://digital-library.theiet.org/files/IET_IPR_CFP_AIPV.pdf Multidisciplinary advancement of Imaging Technologies: From Medical Diagnostics and Genomics to Cognitive Machine Vision, and Artificial Intelligence - https://digital-library.theiet.org/files/IET_IPR_CFP_IST.pdf Deep Learning for 3D Reconstruction - https://digital-library.theiet.org/files/IET_IPR_CFP_DLR.pdf