• Corpus ID: 6180919

Real-time Convolutional Neural Networks for emotion and gender classification

@article{Arriaga2017RealtimeCN,
  title={Real-time Convolutional Neural Networks for emotion and gender classification},
  author={Octavio Arriaga and Matias Valdenegro-Toro and Paul-Gerhard Pl{\"o}ger},
  journal={ArXiv},
  year={2017},
  volume={abs/1710.07557}
}
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. [] Key Method After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique.

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