3D face models, thanks to the accuracy and effectiveness of recent devices and techniques for 3D object reconstruction, are extending and enforcing traditional 2D face recognition engines. Using 3D face models allows, in particular, improving recognition robustness with respect to, e.g. non-frontal or partially occluded acquisitions or variations in the lighting conditions. We further discuss some possible applicative scenarios in the conclusions. In this paper we will describe how a set-up with a single hi-resolution camera together with one, two or more planar mirrors can be implemented to provide accurate 3D models of faces: in particular we will tackle the calibration phase of a multi-mirror environment showing advantages with respect to a multi cameras arrangement, we will also propose a possible reconstruction algorithm which uses a global energy-minimization approach to provide an accurate depth-map accounting for surface smoothness, non-convex regions and holes. Examples carried out with both synthetic and real data show that the proposed approach can fruitfully improve accuracy of 2D recognition engines.

3D Face Reconstruction from a Single Camera Using a Multi Mirror Set-up

MARCON, MARCO;
2009-01-01

Abstract

3D face models, thanks to the accuracy and effectiveness of recent devices and techniques for 3D object reconstruction, are extending and enforcing traditional 2D face recognition engines. Using 3D face models allows, in particular, improving recognition robustness with respect to, e.g. non-frontal or partially occluded acquisitions or variations in the lighting conditions. We further discuss some possible applicative scenarios in the conclusions. In this paper we will describe how a set-up with a single hi-resolution camera together with one, two or more planar mirrors can be implemented to provide accurate 3D models of faces: in particular we will tackle the calibration phase of a multi-mirror environment showing advantages with respect to a multi cameras arrangement, we will also propose a possible reconstruction algorithm which uses a global energy-minimization approach to provide an accurate depth-map accounting for surface smoothness, non-convex regions and holes. Examples carried out with both synthetic and real data show that the proposed approach can fruitfully improve accuracy of 2D recognition engines.
2009
3rd International Conference on Crime Detection and Prevention (ICDP 2009), Proceedings of
9781849192071
minimisation, calibration, cameras, face recognition, image reconstruction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/582673
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