A two-stage fingerprint classification system

Abstract

In this paper we describe a fingerprint classification system based on a two-stage sequential architecture: an MKL-based classifier is first used to select the two-most-likely classes and then a second classifier (specifically trained to discriminate between the two classes) is then adopted for the final decision. The experimentation performed on NIST Special Database 4, which is one of the most important benchmarks in this area, shows that the new approach yields an error rate lower than previously published in the literature. In particular, the error rate is 4.8% and 3.7% for the five-class problem and four-class problem, respectively.