Résumé:
The graduation thesis aims to study and develop a facial expression recognition system using machine
learning. Machine learning techniques are utilized to develop models and algorithms for data analysis,
pattern recognition, and automatic prediction, without the need for direct programming.
In the context of facial recognition and expression analysis, machine learning can be used to develop
models capable of classifying different facial expressions. These models are trained on a large dataset
containing pre-labeled facial expressions. After training on this data, the model becomes capable of
recognizing facial patterns and classifying new expressions presented to it.
Several experimental results have been shared in the thesis, using various classification algorithms such
as SVM and KNN on the FER2013 dataset. The results have demonstrated satisfactory performance using
these algorithms.
To improve these results, a set of filters has been applied to the dataset, and the available data volume has
been increased. Multiple studies have been conducted on these processes, and better results have been
achieved after implementing the enhancements.
In general, it can be concluded that the use of SVM and KNN algorithms in facial expression recognition
has shown satisfactory results. Additional processes such as filter application and data augmentation have
improved performance. This approach holds promise in developing an effective facial expression
recognition system using machine learning. These results and proposed enhancements have been
comprehensively discussed in the thesis.
The research in the thesis aims to expand knowledge and gain a better understanding of machine learning
applications in facial expression recognition. It seeks to achieve further advancements and improvements
in performance and accuracy. This approach is promising and may contribute to the development of more
efficient facial expression recognition systems applicable in various fields.
