TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Arriaga, Octavio
A1  - Valdenegro-Toro, Matias
A1  - Plöger, Paul
T1  - Real-time Convolutional Neural Networks for emotion and gender classification
T2  - 27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019
N2  - 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 classification.
SN  - 978-287-587-065-0
SB  - 978-287-587-065-0
SP  - 221
EP  - 226
S1  - 6
ER  -