Abstract In this paper, a neural fusion rule for fingerprint verification is presented. The person to be identified submits to the system her/his fingerprint and her/his identity. Multiple fingerprint matchers provide a set of verification scores, that are then fused by a perceptronbased method. The weights of such perceptron are explicitly optimised to increase the separation between genuine users and impostors (i.e., unknown users). To this end, the perceptron learning algorithm was modified. Reported experiments show that such modified perceptron allows improving the performances and the robustness of the best individual fingerprint matcher, and outperforming some simple fusion rules.
Perceptron-based fusion of multiple fingerprint matchers
MARCIALIS, GIAN LUCA;ROLI, FABIO
2003-01-01
Abstract
Abstract In this paper, a neural fusion rule for fingerprint verification is presented. The person to be identified submits to the system her/his fingerprint and her/his identity. Multiple fingerprint matchers provide a set of verification scores, that are then fused by a perceptronbased method. The weights of such perceptron are explicitly optimised to increase the separation between genuine users and impostors (i.e., unknown users). To this end, the perceptron learning algorithm was modified. Reported experiments show that such modified perceptron allows improving the performances and the robustness of the best individual fingerprint matcher, and outperforming some simple fusion rules.
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