Imagine trying to identify someone from a blurry security camera video or a smudged fingerprint; usually, it's nearly impossible. This tech combines different clues—like how a person walks, their voice, and their face—to create a digital fingerprint that is much harder to fake or miss. It's like putting together a puzzle where each piece of evidence makes the final picture crystal clear for a judge.
Law enforcement often struggles with 'dirty' biometric data, such as smudged fingerprints or low-quality CCTV footage, which cannot be used as evidence in court. There is also a lack of secure, automated ways to share this sensitive data across different national borders.
A multi-modal biometric fusion engine combining face, voice, and gait analysis. A secure European Biometric Data Space for cross-border evidence sharing and a privacy-preserving storage mechanism for biometric templates.
If you are a police agency dealing with incomplete evidence from crime scenes—this project developed a fusion engine that combines face, gait, and voice data to provide court-proof identification. This ensures suspects are identified even when fingerprints are smudged.
If you are a forensics firm dealing with encrypted or locked seized mobile devices—this project developed methodologies to extract behavioral patterns and location traces. This allows investigators to link a physical device to a specific owner more accurately.
If you are a border authority dealing with identity fraud—this project developed a European Biometric Data Space for seamless sharing of biometric intelligence. This allows for faster and more accurate identification of suspects across 14 different countries.
Based on available project data, no specific commercial pricing or cost per license is mentioned; the project received an EU contribution of EUR 4,562,975 for development.
Yes, the objective specifically mentions creating a scalable exchange of biometric intelligence and a data space to enable seamless collaboration among security practitioners.
Based on available project data, the specific IP and licensing terms are not disclosed, though it involves a consortium of 24 partners including 13 industry players.
The project includes a biometric data protection mechanism that allows for the revocability of biometric templates to ensure privacy-preserving storage.
It is designed to enable seamless collaboration among existing systems used by security practitioners and Forensic Institutes through a common data space.
The consortium is heavily weighted toward commercial application, with 13 industry partners representing a 54% industry ratio. With 24 partners across 14 countries, the project has a strong cross-border footprint, suggesting the resulting tools are designed for international interoperability and market adoption rather than pure academic research.
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