Sentiment analysis from face expressions based on image processing

In this thesis, the classification study of human facial expressions in real-time images was investigated. Real-time recognition has a number of benefits, for example; analysis of mood in group photos is an interesting example in this regard. The perception of facial expressions of people in photographs taken during an event can provide quantitative data on how much fun these people have in general. Another example is context-aware image access, where, for example, only photos of people who are surprised can be accessed from a database. This scope of work; 7 different emotions related to facial expressions were classified; these are listed as "happiness", "sadness", "surprise", "disgust", "anger", "fear" and "neutral". With the application written in Python programming language, classical machine learning methods k-Nearest Neighborhood and Support Vector Machines and AlexNet, ResNet, DenseNet, Inception architectures are used from deep learning methods and detailed analyzes are made in FER2013, JAFFE and CK+ datasets. In this study, while comparing classical machine learning methods and deep learning architectures, real-time and non-real-time applications were also compared with two different applications. This study shows that real-time expression recognition systems based on deep learning techniques with the most appropriate architecture can be implemented with high accuracy via computer hardware with only one software.