Farenzena, M.Cristani, M.Castellani, U.Fusiello, A.Raffaele De Amicis and Giuseppe Conti2014-01-272014-01-272007978-3905673-62-3https://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2007/039-043In this paper, a novel approach to face clustering is proposed. The aim is the completely unsupervised extraction of planes in a polygonal a mesh, obtained from a 3D reconstruction process. In this context, 3D coordinates points are inevitably affected by error, therefore resiliency is a primal concern in the analysis. The method is based on the Mean Shift clustering paradigm, devoted to separating modes of a multimodal non-parametric density, by using a kernel-based technique. A critical parameter, the kernel bandwidth size, is here automatically detected by following a well-accepted partition stability criterion. Experimental and comparative results on synthetic and real data validate the approach.Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computational Geometry and Object Modeling]: