This thesis deals with facial recognition using convolutional neural networks and with their current problems, which are pose, lighting and expression variance. It summarizes existing approaches, architectures and most recent loss functions. Further it deals with methods for rotating faces using GAN networks. In this thesis 3 neural networks are designed and trained for facial recognition. The best of them achieves 99.38% accuracy on LFW dataset and 88.08% accuracy on CPLFW dataset. Next face rotation network PCGAN is designed, which can be used for face frontalization or data augmentation purposes. This network is evaluated on Multi-PIE dataset and using the face frontalization it increases identification accuracy.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:399176 |
| Date | January 2019 |
| Creators | Stratil, Jan |
| Contributors | Špaňhel, Jakub, Hradiš, Michal |
| Publisher | Vysoké učení technické v Brně. Fakulta informačních technologií |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
Page generated in 0.0026 seconds