Performance evaluation of machine learning for recognizing human facial emotions

Facial Expression Recognition is a human emotion classification problem that attracted much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are there still the main issues when extracting features of human emotions. To cope with these challenges, we propose an automatic system that provides users with well-adopted classifier for recognizing facial expressions more accurately. The system consists of two fundamental machine-learning stages, namely, feature selection and feature classification. Feature selection is performed using Active Shape Model (ASM) composed of landmarks while the feature classification has examined seven well-known classifiers. We have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. Experimental results showed that Quadratic classifier provides excellent performance and outperforms other classifiers with the highest accuracy of 92.42% on the same dataset. © 2020 Lavoisier. All rights reserved.