L’augmentation des données pour les systèmes de reconnaissances des expressions faciales

Abstract

Since the emergence of deep learning approaches, facial expression recognition has grown significantly, and these systems have become increasingly more efficient. However, effective learning requires large databases which are not always available. To address this, databases need to be augmented using appropriate data augmentation techniques (DA), which are typically based on geometric transformations or oversampling augmentations. These techniques allow to increase the size of the training database and to rebalance it. In this project, we propose to explore and evaluate the use of three data augmentation techniques, as well as the effect of their hybridization, on a facial expression recognition system. Two deep learning models have been used for this evaluation, the VGG16 and the Resnet50. The augmentation techniques tested are geometric transformations, SMOTE and Cycle-Gan. The results obtained show that the operation of the RMF systems requires a large volume of data for learning and that GANs offer an interesting alternative to overcome this data deficit.