Semantic analisis on faces using deep neural networks

• Autores: Nicolás Federico Pellejero, Guillermo Grinblat, Lucas Uzal
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 21, Nº. 61, 2018 (Ejemplar dedicado a: Inteligencia Artificial (June 2018)), págs. 14-29
• Idioma: inglés
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• Resumen

  • 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.