Planar Shoeprint Segmentation Based on the Multiplicative Intrinsic Component Optimization

Abstract

Shoeprint is an important trace evidence in forensic science. It can provide information about age, height and sex of suspects. In order to solve the problem of individual differences in the identification of planar-shoeprint experts doing, a planar shoeprint image segmentation algorithm based on Multiplicative Intrinsic Component Optimization is proposed in this context. After segmentation, pseudo-color can be selectively used to process the segmentation image. So the pattern and wear area of sole were automatically sketched. Experimental analysis shows that this method can effectively segment the shoeprint. This provides an objective and universal shoeprint identification method for criminal investigators to narrow the scope of investigation.