IDEMIA brings cutting-edge fingerprint-matching algorithms to local law enforcement agencies

IDEMIA, the biometric solutions pioneer for law enforcement agencies, announced it has launched the world’s first SaaS-based Automated Biometric Identification System (ABIS), IDEMIA STORM ABIS™, for intuitive, accessible, and affordable fingerprint analysis, comparison, and documentation. 

Intuitive web-based platform

IDEMIA STORM ABIS is an intuitive web-based platform that allows for efficient access to world-class fingerprint-matching technology. It supports local and national searches with tools for analysis, comparison, and case management that allow fingerprint examiners to efficiently complete examinations from anywhere.

With user experience as a design priority, the system enhances collaboration while reducing the learning curve for examiners, which cuts training time and costs for agencies. 

Automated fingerprint identification capabilities

IDEMIA STORM ABIS streamlines the system deployment and delivery process with standardised configurations “IDEMIA has delivered high-performing biometric identification systems hosted in the cloud since 2015. Yet IDEMIA STORM ABIS is the first of its kind as its cloud-native architecture allows dynamic scalability, high availability, and system reliability on-demand,” said Michael Hash, vice president, justice and public safety, IDEMIA.

“Designed from the ground up by fingerprint examiners for examiners, STORM will transform how we deliver automated fingerprint identification capabilities to our law enforcement customers.”

Standardised configurations

As an out-of-box SaaS ABIS, IDEMIA STORM ABIS drastically streamlines the system deployment and delivery process with standardised configurations.

Leveraging native cloud architecture, this new SaaS offering offers continuous feature updates and allows smaller agencies to use the biometric identification services securely from anywhere, enabling flexible and remote work environments, a useful feature in the post-COVID world.