Algorithm transparency

Algorithm transparency in SourceAFIS is an API that lets applications capture all intermediate data structures that are created during feature extraction and matching, including images in various stages of filtering, extracted minutiae and ridges, pairing constructed by matcher, and score breakdown. Learning basics of how the algorithm works is recommended before using transparency API.

Motivation

The simple API exposed by SourceAFIS is generally an advantage, because the job of SourceAFIS is to abstract away details of fingerprint matching. There are however occasions when seeing what the algorithm is doing is beneficial. Specifically, algorithm transparency can be used to:

• explain how the algorithm reached its decision,
• create rich visualizations of fingerprints and their matches,
• check quality of captured fingerprints,
• use fingerprint features creatively for something other than matching,
• support fingerprint experts in manual matching,
• reconstruct damaged fingerprints in forensics,
• diagnose issues with sensors, outlier populations, and algorithm itself, and
• export data for use in another biometric system.

Transparency data is also used internally to ensure consistency of language ports of SourceAFIS and to perform basic sanity checks in unit tests.

Algorithm transparency is rare in commercial matchers, because it weakens intellectual property protection. SourceAFIS, being fully opensource, doesn't need to hide anything from you. Algorithm transparency is its unique advantage.

Capture

Every language port of SourceAFIS has core transparency API, which is introduced in its tutorial:

• SourceAFIS for Java
• SourceAFIS for .NET

Some language ports can produce transparency ZIP.

Dedicated library for manipulating transparency data is usually more convenient:

• SourceAFIS Transparency for Java

If all you need is visualization, you can use visualization library:

• SourceAFIS Visualization for Java

Excluded data

Transparency data does not include data that is available in some other way. Specifically, it excludes the original fingerprint image, source byte array during deserialization, the resulting template (which is documented elsewhere), and the final similarity score. You have to capture this data separately.

Data keys

Available transparency data is documented on subpages linked below.