Facial expression classification based on LDA-GS-SVM

Fanchen Zheng

doi:10.1117/12.3004564

11 October 2023 Facial expression classification based on LDA-GS-SVM

Fanchen Zheng

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280044 (2023) https://doi.org/10.1117/12.3004564
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

With the development of artificial intelligence (AI), facial expression recognition has become a focus of current research owing to its wide application potential. In this study, based on the karolinska directed emotional faces KDEF face public dataset, we adopted a parameter optimization method to optimize the kernel function parameters of the support vector machine which was used for classification. This study also conducted a comprehensive comparative analysis of the effects of expression recognition before and after parameter optimization. First, traditional manual and deep learning methods were used to extract face features, form two feature sets, and combine them to obtain a hybrid feature set. Second, linear discriminant analysis was used to reduce dimensionality, and a correlation matrix was used to select the class vectors to remove redundant features. Subsequently, grid search and k-fold cross-validation methods were used to optimize the parameters. The kernel function with default parameters and the parameter-optimized kernel function were used to classify the feature sets, and the effects of parameter optimization on facial expression classification were compared and analyzed. Finally, the classification effect of support vector machines with different parameters was evaluated. The results show that the overall classification effect of support vector machine is good and that parameter optimization can improve the accuracy of expression classification to a certain extent. The classification accuracy of the test set for the parameter-optimized support vector machine can be as high as 98.8%, indicating good applicability to the dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fanchen Zheng "Facial expression classification based on LDA-GS-SVM", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280044 (11 October 2023); https://doi.org/10.1117/12.3004564

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KEYWORDS

Feature extraction

Facial recognition systems

Matrices

Support vector machines

Analytical research

Cross validation

Databases

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