In this work we propose a local approach of 2D ear authentication. A multi-matcher system is proposed where each matcher is trained using features extracted from a single sub-window of the whole 2D image. The features are extracted by the convolution of each sub-window with a bank of Gabor Filters, then their dimensionality is reduced by Laplacian EigenMaps. The best matchers, corresponding to the most discriminative sub-windows, are selected by running the Sequential Forward Floating Selection (SFFS). Our experiments, carried out on a database of 114 people, show that combining only few (~ten) sub-windows in the fusion step it is possible to achieve a very low Equal Error Rate.

A multi-matcher for Ear Authentication

NANNI, LORIS;LUMINI, ALESSANDRA
2007

Abstract

In this work we propose a local approach of 2D ear authentication. A multi-matcher system is proposed where each matcher is trained using features extracted from a single sub-window of the whole 2D image. The features are extracted by the convolution of each sub-window with a bank of Gabor Filters, then their dimensionality is reduced by Laplacian EigenMaps. The best matchers, corresponding to the most discriminative sub-windows, are selected by running the Sequential Forward Floating Selection (SFFS). Our experiments, carried out on a database of 114 people, show that combining only few (~ten) sub-windows in the fusion step it is possible to achieve a very low Equal Error Rate.
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Nanni, Loris; Lumini, Alessandra
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/54622
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