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

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 o f 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 o f 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 i s a fully automatic method and functions with minimum user intervention. Tests o f 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% o f displayed shoeprints 75% o f the time. The system has translational and rotational invariance so that the spatial positioning o f the new shoeprint images does not have to correspond with the spatial positioning o f the shoeprint images of the database.