Facial Expression Recognition Techniques and Comparative Analysis Using Classification Algorithms

Abstract

With the development of technology and hardware possibilities, it has become possible to analyze the changes that occur as a result of the reflection of emotional state on facial expression with computer vision applications. Facial expression analysis systems are used in applications such as security systems, early diagnosis of certain diseases in the medical world, human-computer interaction, safe driving. Facial expression analysis systems developed using image data consist of 3 basic stages. These; extracting the face area from the input data, extracting the feature vectors of the data and classifying the feature vectors. In this study, the features of the dataset were obtained with the AlexNet model, which is one of the deep learning models that achieved successful results in classification problems. In the study in which the comparative analysis of the obtained results is presented, accuracy of 89.7%, 87.8% and 81.7% was obtained with machine learning techniques.