Publikation

Real-time Convolutional Neural Networks for emotion and gender classification

Luis Octavio Arriaga Camargo, Matias Alejandro Valdenegro Toro, Paul G. Plöger

In: ESANN 2019 Proceedings. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-2019) The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning April 24-26 Brügge Belgium i6doc 4/2019.

Abstrakt

Emotion and gender recognition from facial features are important properties of human empathy. Robots should also have these capabilities. For this purpose we have designed special convolutional modules that allow a model to recognize emotions and gender with a considerable lower number of parameters, enabling real-time evaluation on a constrained platform. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset, while requiring a computation time of less than 0:008 seconds on a Core i7 CPU. All our code, demos and pre-trained architectures have been released under an open-source license in our repository at https://github.com/oarriaga/face classi cation.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence