Automatic classification of partial shoeprints using Advanced Correlation Filters for use in forensic science

One of the most difficult problems in automatic shoeprint classification is the matching of partial shoeprint images. This task becomes more challenging in the presence of geometric distortions (e.g. translated and/or rotated partial prints). In this paper, we evaluate the performance of advanced correlation filters (ACFs) for the automatic classification of partial shoeprints. The optimal trade-off synthetic discriminant function (OTSDF) filter and the unconstrained OTSDF (UOTSDF) filter, in particular, were used to match partial shoeprint images with different qualities. Experimental assessment using a shoeprint image database has demonstrated the efficient classification performance of ACFs compared to other state-of-the-art methods.