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, (ESANN-2019), 24.4.-26.4.2019, Brügge, i6doc, Apr/2019.

Abstract :

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.



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