PRA Lab works on the development of next generation pattern recognition systems for real applications such as biometric authentication, text categorization, and intrusion detection in computer networks. PRA mission is to address fundamental issues for the development of future pattern recognition systems, in the context of real applications.
Many biometric recognition systems require using a small set of reference templates per client to save computational resources during client verification. PRA Lab's approach, Super-sparse Biometric Recognition
, is capable of outperforming state-of-the-art methods both in terms of recognition accuracy and number of required reference templates, by jointly learning an optimal combination of matching scores and the corresponding subset of templates.
PRA Lab has 19 years experience
on the development of next-generation Pattern Recognition systems. The Lab Director is Prof. Fabio Roli
, IEEE and IAPR fellow. The Lab is made up of more than 30 people, including faculty members, post-doc researchers, PhD students and lab fellows. Research activities are carried out in the framework of regional, national, and european projects funded by public as well as private initiatives. Read more about our researchers
"There is nothing more practical than a good theory".
Pra Lab develops many tools for computer security. SuStorID
is an advanced Intrusion Detection System (IDS) for web services, based on machine learning. It demonstrates a number of interesting features, that can be readily exploited to detect and act against web attacks: Autonomous Learning
- Anomaly-based Approach
- Multi-model Architecture
- Real-time Counteractions
- Easy integration with modsecurity
- Inspection of Encrypted traffic
- User-friendly Interface
Pra Lab works on Biometric Security
creating personal identification and authentication systems, face and fingerprint recognition solutions designed to be resilient against spoofing attacks.
Research at PRA Lab aims to develop secure-by-design systems
, natively resilient against the attempts of evasion made by adversaries. The Lab activities focused on the “Adversarial Learning
” area aim to study how the learning algorithms that empower our systems can be made more robust against these attempts by proactively simulating an arms race with the adversary to meet more strict security requirements.