Classification of Facial Images and Moods Using Image-Based Algorithms

Emotion analysis is a subject area that has been frequently emphasized recently, aiming to extract human emotions from images. In this study, an attempt was made to develop a model using deep learning methods to classify people’s emotional expressions in the images in the data set. In the study, three different deep learning algorithms were tested: CNN, ResNet50 and VGG19. Afterwards, the training results of all three models are presented and the hyperparameters and training parameters are explained. The methods section of the article describes in detail the dataset used, data preprocessing steps, and the architecture of the machine learning model. Data collection, labelling, and data augmentation strategies are presented in detail. Then, the training process is described for all three models, including hyperparameters and training parameters. The results section presents the performance of all three models on the sentiment analysis task. After training, the success rates of CNN, VGG19 and ResNET50 algorithms are 98.64%, 98.58% and 98.19%, respectively. The results show how effective each model is at successfully classifying emotional expressions. Additionally, the advantages and limitations of each model are discussed.