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 algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions%2C such as the subject's distance from the camera or the camera's characteristics%2C are different than expected%2C as typically happens in video surveillance. Additionally%2C 3DFR algorithms follow various strategies to address the reconstruction of a 3D shape from 2D data%2C such as statistical model fitting%2C photometric stereo%2C or deep learning. In the present study%2C we explore the application of three 3DFR algorithms representative of the SOTA%2C employing each one as the template set generator for a face verification system. The scores provided by each system are combined by score-level fusion. We show that the complementarity induced by different 3DFR algorithms improves performance when tests are conducted at never-seen-before distances from the camera and camera characteristics (cross-distance and cross-camera settings)%2C thus encouraging further investigations on multiple 3DFR-based approaches.?quality=80&w=800)
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