TY  - JOUR
AU  - Meysam Madadi
AU  - Sergio Escalera
AU  - Jordi Gonzalez
AU  - Xavier Roca
AU  - Felipe Lumbreras
PY  - 2015//
TI  - Multi-part body segmentation based on depth maps for soft biometry analysis
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 14
EP  - 21
VL  - 56
KW  - 3D shape context
KW  - 3D point cloud alignment
KW  - Depth maps
KW  - Human body segmentation
KW  - Soft biometry analysis
N2  - 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.
UR  - http://dx.doi.org/10.1016/j.patrec.2015.01.012
N1  - HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB
ID  - Meysam Madadi2015
ER  -