Semantic analysis on faces using deep neural networks

Análisis semántico en rostros utilizando redes neuronales profundas.

Authors

  • Nicolás Federico Pellejero Universidad Nacional de Rosario
  • Guillermo Grinblat
  • Lucas Uzal

DOI:

https://doi.org/10.4114/intartif.vol21iss61pp14-29

Keywords:

Deep, Learning, Emotion, Recognition.

Abstract

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.

Downloads

Download data is not yet available.

Downloads

Published

2018-03-21

How to Cite

Pellejero, N. F., Grinblat, G., & Uzal, L. (2018). Semantic analysis on faces using deep neural networks: Análisis semántico en rostros utilizando redes neuronales profundas. Inteligencia Artificial, 21(61), 14–29. https://doi.org/10.4114/intartif.vol21iss61pp14-29