Semantic analysis on faces using deep neural networks

Análisis semántico en rostros utilizando redes neuronales profundas.

  • Nicolás Federico Pellejero Universidad Nacional de Rosario
  • Guillermo Grinblat
  • Lucas Uzal
Keywords: Deep, Learning, Emotion, Recognition.


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.


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How to Cite
Pellejero, N., Grinblat, G., & Uzal, L. (2018). Semantic analysis on faces using deep neural networks. Inteligencia Artificial, 21(61), 14-29.