FEDARS: Feature Extraction from Deep learning Architectures for face Recognition Systems

• Unité de recherche: INNOSUISSE
• Numéro de projet: 14753.1;8 PFES-ES
• Titre du projet anglais: FEDARS: Feature Extraction from Deep learning Architectures for face Recognition Systems

Description succincte (Anglais)

FEDARS: Feature Extraction from Deep learning Architectures for face Recognition Systems

Description succincte (Français)

FEDARS: Feature Extraction from Deep learning Architectures for face Recognition Systems

Résumé des résultats (Abstract) (Anglais)

This porject FEDARS is to develop new methods to extract robust features for face recognition systems. Deep learning architectures will be investigated to learn new features from the data. These new features will be integrated into KeyLemon¿s product line to improve the efficiency of their core technology and to enable KeyLemon to reach new market segment (banking and finance). This project will be part of the larger EUREKA European project PRI-BIOSEC.

Résumé des résultats (Abstract) (Français)

This porject FEDARS is to develop new methods to extract robust features for face recognition systems. Deep learning architectures will be investigated to learn new features from the data. These new features will be integrated into KeyLemon¿s product line to improve the efficiency of their core technology and to enable KeyLemon to reach new market segment (banking and finance). This project will be part of the larger EUREKA European project PRI-BIOSEC.