Abstract: A Bayesian belief network-based architecture for multimodal biometric fusion is disclosed. Bayesian networks are a theoretically sound, probabilistic framework for information fusion. The architecture incorporates prior knowledge of each modality's capabilities, quality estimates for each sample, and relationships or dependencies between these variables. A global quality estimate is introduced to support decision making.

Abstract: The invention provides methods and systems for performing a matching function between a first pattern and a second pattern. This technique involves creating a set of all distance measurements between pairs of points in the first pattern and all distance measurements between pairs of points in the second pattern. This set is then partitioned into subsets of nearly equal distance elements. Those subsets containing at least one element derived from each pattern determine possible line segment matches which are then analyzed mathematically to determine the partial transform that maps the associated points of the first pattern into the points of the second pattern. The resulting set of partial transforms is then reviewed to determine matched line segments between the two patterns.