We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs), and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features which can be integrated in the SVMs. SVMs are combined with a new error correcting code scheme which, unlike previous systems, can also exploit information contained in ambiguous fingerprint images. Experimental results indicate the benefit of integrating global and structured representations and suggest that SVMs are a promising approach for fingerprint classification.

A New Machine Learning Approach to Fingerprint Classification

MARCIALIS, GIAN LUCA;ROLI, FABIO
2001

Abstract

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs), and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features which can be integrated in the SVMs. SVMs are combined with a new error correcting code scheme which, unlike previous systems, can also exploit information contained in ambiguous fingerprint images. Experimental results indicate the benefit of integrating global and structured representations and suggest that SVMs are a promising approach for fingerprint classification.
978-3-540-42601-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/99527
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