Recognition of learning-centered emotions using a convolutional neural network

This work presents the application of a convolutional neural network (CNN) used to identify emotions through taken images to students, which are learning Java language with an Intelligent Learning Environment. The CNN contains three convolutional layers, three max-pooling layers, and three neural networks with intermediate dropout connections. The CNN was trained using different emotional databases. One of them was a posed database (RaFD) and two of them were spontaneous databases created specially by us with a content focused on learning-centered emotions. The results show a comparison among three emotion recognition systems. One applying a local binary pattern approach with facial patches, another applying a geometry-based method, and the last one applying the convolutional network. The analysis presented satisfactory results; the CNN obtained a 95% accuracy for the RaFD database, an 88% accuracy for a learning-centered emotion database and a 74% accuracy for a second learning-centered emotion database. Results are compared against the classifiers support vector machine, k-nearest neighbors, and artificial neural network.