Heat Diffusion Approach for Feature-based Body Scans Analysis

In this paper we propose the use of spectral shape analysis techniques for selection and classification of anthropometric points extracted from human-body scans. Few feature points are detected by exploiting the capability of heat diffusion process in capturing the extremities of surface protrusions which are often related to anthropometric landmarks. Then, a heat kernel signature is computed for each feature point which are associated to its semantic group by employing a learning-by-example procedure exploiting manual point labeling provided by an expert anthropometrist. Detected points are not clearly the same precise anatomical locations used in standard anthropometric procedures, but their matching can be useful for different applications like automatic model registration or simple body type evaluation. Experimental tests carried out on several subjects with different anthropometric characteristics show encouraging results demonstrating the potential usefulness of the approach as well as the necessity of further investigation on point description and matching.