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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Yao, Y., Marcialis, G.L., Pontil, M., Frasconi, P., Roli, F. (2001). A New Machine Learning Approach to Fingerprint Classification. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_7
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DOI: https://doi.org/10.1007/3-540-45411-X_7
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