Real-time Convolutional Neural Networks for emotion and gender classification
- 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.
| Document Type: | Conference Object |
|---|---|
| Language: | English |
| Author: | Octavio Arriaga, Matias Valdenegro-Toro, Paul Plöger |
| Parent Title (English): | 27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019 |
| Number of pages: | 6 |
| First Page: | 221 |
| Last Page: | 226 |
| ISBN: | 978-287-587-065-0 |
| Date of first publication: | 2019/04/24 |
| Departments, institutes and facilities: | Fachbereich Informatik |
| Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Entry in this database: | 2020/07/17 |
