Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions

Facial expression recognition is a human emotion classification problem attracting much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are still the issues when extracting features of human emotions. To cope with these challenges, the authors propose an automatic system that provides users with a well-adopted classifier for recognizing facial expressions in a more accurate manner. The system is based on two fundamental machine learning stages, namely feature selection and feature classification. Feature selection is realized by active shape model (ASM) composed of landmarks while the feature classification algorithm is based on seven well-known classifiers. The authors have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. The experimental results show that quadratic classifier (DA) provides excellent performance, and it outperforms the other classifiers with the highest recognition rate of 100% for the same dataset.