A learning framework for adaptive fingerprint identification using relevance feedback

In recent years, law enforcement personnel have greatly been aided by the deployment of Automated Fingerprint Identification Systems (AFIS). These systems largely operate by matching salient features automatically extracted from fingerprint images for their decision. However, there are two major shortcomings in current systems. First, the result of identification depends primarily on the chosen features and the algorithm that matches them. Second, these systems cannot improve their results by benefiting from interactions with seasoned examiners who often can identify minute differences between fingerprints beyond that is capable of by current systems. In this paper, we propose a system for fingerprint identification that incorporates relevance feedback. We show that a persistent semantic space over the database of fingerprints can be incrementally learned. Here, the learning module makes use of a dimensionality reduction process that returns both a low-dimensional semantic space and an out-of-sample mapping function, achieving a two-fold benefits of data compression and the ability to project novel fingerprints directly onto the semantic space for identification. Experimental results demonstrated the potential of this learning framework for adaptive fingerprint identification.