Automatic classification of shoeprints for use in forensic science based on the fourier transform

Abstract

This study developed a system of automatic classification of shoeprint images into groups belonging to the same sole pattern. When presented with an image of a new shoeprint the system displays a ranked sequence of shoeprint images from the database. The shoeprint images are ranked from best match to worst match in terms of the pattern of the shoeprint. For this study a database of 503 shoeprint images belonging to 139 pattem groups was established with each group containing 2 or more examples. The pattern grouping was performed by a panel of human experts. This designed system is a fully automatic method and functions with minimum user intervention. Tests of the system have shown that the first shoeprint image displayed is a correct match 54% of the time and that a correct match appears within the first 5% of displayed shoeprints 75% of the time. The system has translational and rotational invariance so that the spatial positioning of the new shoeprint images does not have to correspond with the spatial positioning of the shoeprint images of the database.