%0 Journal Article
%T Multi-part body segmentation based on depth maps for soft biometry analysis
%A Meysam Madadi
%A Sergio Escalera
%A Jordi Gonzalez
%A Xavier Roca
%A Felipe Lumbreras
%J Pattern Recognition Letters
%D 2015
%V 56
%F Meysam Madadi2015
%O HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2588), last updated on Wed, 28 Oct 2015 16:13:20 +0100
%X This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.
%K 3D shape context
%K 3D point cloud alignment
%K Depth maps
%K Human body segmentation
%K Soft biometry analysis
%U http://dx.doi.org/10.1016/j.patrec.2015.01.012
%P 14-21