Towards the Construction of an Emotion Analysis Model in University Students Using Images Taken in Classrooms

Data mining is used in various fields, image processing is one of them, a particular application is the identification and classification of emotions expressed by students in the classroom. However, this creates challenges, such as the subjective interpretation of facial expressions and the need for extensive data sets to train and validate the models, for the former it is required to go to other allied research fields, and for the latter, a possibility is glimpsed in the transfer of learning. This work seeks to review and compare different classifiers for the construction of a model that allows the analysis of the emotions of university students from images extracted from recordings of face-to-face classes stored in an educational support platform. For this, the KDD (Knowledge Discovery in Databases) methodology was followed, and experiments were proposed with different configurations of hyperparameters and generation of models from classifiers such as Nearby Neighbors-KNN, Convolutional Neural Networks-CNN, and Random Forest. The performance of each one is contrasted based on precision, recall, F1, Accuracy, and ROC curve. Additionally, an approximation to a learning transfer process was carried out using an open-use data set (taken from the Kaggle repository) for the classification of emotions for the training of the models and validating with the data extracted from the source of the case study. The results support the utility and potential of applying these techniques in scenarios where image-based emotion analysis is required, with CNN being the classifier with the best accuracy and obtaining significant value from knowledge transfer that motivates further deepening of the approach for the treatment of this problem.