Multi-part body segmentation based on depth maps for soft biometry analysis

Meysam Madadi

doi:10.1016/j.patrec.2015.01.012

Multi-part body segmentation based on depth maps for soft biometry analysis

2015-04-01

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DOI：10.1016/j.patrec.2015.01.012

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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. (C) 2015 Elsevier B.V. All rights reserved.

Keywords:

3D shape context

3D point cloud alignment

Depth maps

Human body segmentation

Soft biometry analysis

Multi-part body segmentation based on depth maps for soft biometry analysis

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