Semantic analysis on faces using deep neural networks

In this paper we address the problem of automatic emotion recognition and classification through video. Nowadays there are excellent results focused on lab-made datasets, with posed facial expressions. On the other hand there is room for a lot of improvement in the case of 'in the wild' datasets, where light, face angle to the camera, etc. are taken into account. In these cases it could be very harmful to work with a small dataset. Currently, there are not big enough datasets of adequately labeled faces for the task. We use Generative Adversarial Networks in order to train models in a semi-supervised fashion, generating realistic face images in the process, allowing the exploitation of a big cumulus of unlabeled face images.