In this paper a suitable methodology for the improvement of the reliability of results in classification systems based on 3D images is proposed. More in detail, it is based on the knowledge of the uncertainty of the features constituting the 3D image (obtained processing a pair of two 2D stereoscopic images) and on a suitable statistical approach providing a confidence level to the classification result. These pieces of information are then managed in order to improve the classification performance in terms of correct classification and false reject percentages. The experimental results, obtained applying the methodology on an Active Appearance Models algorithm for feature detection and triangulating the 3D features, show that, compared with a basic approach (which generally does not take into account the uncertainty on 3D features), the proposed methodology allows to significantly improve the classification performance even in scenarios characterized by a high uncertainty. © 2015 Elsevier Ltd. All rights reserved.

Face recognition based on 3D features: Management of the measurement uncertainty for improving the classification

BETTA, GIOVANNI;CAPRIGLIONE, DOMENICO;LIGUORI, CONSOLATINA;PAOLILLO, Alfredo
2015

Abstract

In this paper a suitable methodology for the improvement of the reliability of results in classification systems based on 3D images is proposed. More in detail, it is based on the knowledge of the uncertainty of the features constituting the 3D image (obtained processing a pair of two 2D stereoscopic images) and on a suitable statistical approach providing a confidence level to the classification result. These pieces of information are then managed in order to improve the classification performance in terms of correct classification and false reject percentages. The experimental results, obtained applying the methodology on an Active Appearance Models algorithm for feature detection and triangulating the 3D features, show that, compared with a basic approach (which generally does not take into account the uncertainty on 3D features), the proposed methodology allows to significantly improve the classification performance even in scenarios characterized by a high uncertainty. © 2015 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4656454
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