KIDOLEH is a joint research and innovation project aimed at significantly improving finger vein authentication (FVA) through advanced machine learning techniques.
The project focuses on enhancing Global ID's F2 protocol, a finger vein imaging device, by integrating a new data processing framework designed to achieve high levels of security and speed for critical environments such as hospitals and secure infrastructures.
The new methods developed are evaluated offline using data collected by the F2 device, with the objective of increasing identity verification accuracy while achieving comparison speeds fast enough to support efficient, large-scale operational deduplication.
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