Title: Adaptive semi-supervised facial expression recognition method based on improved ResNet50

Authors: Z.Q. Lin; S.W. Wang

Addresses: College of Science, Shanghai Institute of Technology, Shanghai, China ' College of Science, Shanghai Institute of Technology, Shanghai, China

Abstract: This study addresses the limitations of single convolutional neural networks in deep learning, which struggle with inadequate extraction of features from imbalanced expression labels and exhibit recognition errors when subjected to disturbances. The proposed solution introduces an adaptive semi-supervised facial expression recognition model. This model adeptly extracts expression features from unbalanced datasets, mitigates overfitting, and thus enhances overall expression recognition accuracy. By incorporating a self-attention mechanism, optimising convolutional kernels, and introducing replacement activation functions within the ResNet50 network, both computational efficiency and feature extraction are significantly improved. Moreover, the application of the adaptive semi-supervised method within training refines the accuracy of the model and prevents overfitting, thereby bolstering its robustness. Experimental findings indicate that the adaptive semi-supervised network, based on the enhanced ResNet50, achieves recognition rates of 73% and 99.57% on the FER2013 and JAFFE facial expression datasets, respectively. Comparative analysis with traditional single convolutional neural networks like ResNet18, VGG16, and VGG19, as well as optimised networks like IL-CNN, reveals an overall accuracy improvement of 2-7% and 1-4%, respectively.

Keywords: semi-supervised learning; expression recognition; residual networks; adaptive methods; convolution kernel.

DOI: 10.1504/IJISTA.2024.140951

International Journal of Intelligent Systems Technologies and Applications, 2024 Vol.22 No.3, pp.313 - 332

Received: 17 Aug 2023
Accepted: 02 Nov 2023
Published online: 04 Sep 2024 *

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