Welcome to CODE, Prof. Gomez-Barrero

24 October 2023

Prof. Dr. Marta Gomez-Barrero has accepted the W3 professorship for Machine Learning at the Faculty of Computer Science at the University of the Bundeswehr Munich (UniBw M) as of October 1, 2023. Upon her appointment, Prof. Gomez-Barrero gave an interview in which she talks about her research and future plans.

Aktuelles

IEEE IWBF 2025 in Munich!

The BioML Lab will host the 2025 IEEE International Workshop on Biometrics and Forensics (IWBF 2025) on April 24-25, 2025, in Munich (Germany).

Welcome to BioML, Osman Demir!

On March 2024, Osman Demir joined the BioML Lab to pursue his PhD and do research on attack detection and privacy protection in biometric recognition systems.

The revision of ISO/IEC 30136 has started!

Biometric Template Protection (BTP) remains a very important topic for operational biometric systems. The revision of ISO/IEC 30136 Performance testing of biometric template protection schemes has started, being Marta Gomez-Barrero the project's editor. At BioML, we are looking forward to the integration of the unlinkability testing methodology.

Welcome to BioML, Erik Trolliet!

On February 2024, Erik Trolliet joined the BioML Lab to pursue his PhD and do research on the generation of synthetic biometric samples and how to use them to enhance biometric recognition systems.

ISO/IEC SC 37 meets in Okayama, Japan

Between January 8 and 12, 2024, the ISO/IEC SC 37 on biometrics met in Okayama, Japan.

WIFS 2023 in Nürnberg

The IEEE International Workshop on Information Security and Forensics (WIFS), was co-organised by Christian Riess (FAU) and Marta Gomez-Barrero on December 4 to 7, 2023, un Nürnberg

We are hiring!

We are looking for a PhD student in computer science on the topic of biometric template protection.The aim is to develop new algorithms to improve privacy protection in biometric recognition systems based on the state of the art in both biometric recognition (e.g. deep learning architectures) and modern cryptography (post-quantum cryptography, differential learning).