Multiview segmented filter for multicorrelation: application to 3D face recognition

Abstract

A novel approach to realize an optimized multi-decision segmented filter for recognizing a 3D object in a given scene is suggested. For that purpose, a correlation filter suitable for multi-view images is proposed. Like 3D object reconstruction techniques from multi-view images, we use multiple cameras displayed in a well-defined pickup grid. Each multi-view image gives a unique perspective of the scene. Then, the elemental images of the 3D object are separately correlated using a multi-channel correlator with the newly designed optimized filter. This optimization was carried out to take into account possible changes of the 3D object in the scene, e.g. rotation, scale, because correlation techniques are very sensitive to this kind of noise. To solve this problem, the filter must be sufficiently flexible to include information about different rotations of the elemental image. The optimization of the fusion of the different elemental references images, e.g. taken from different angles of rotation, was realized. In addition, a shifting of the different spectra was done in the filter plane to minimize the overlap between the different elemental spectra. In this study, particular attention is paid for the choice of the lateral shifting. With this correlation system, each channel gives a single decision concerning a single perspective of the 3D object. We demonstrate that the final correlation decision takes into account all these elemental decisions to determine the presence (or not) of the 3D object in the target scene.