A combined approach for footwear retrieval of crime scene shoe marks

Shoeprints found on the crime scene are useful to understand the crime dynamics; currently some systems have been proposed to automatically identify the make and model of the shoe that left the mark on the crime scene. However these systems have been tested on shoe marks synthesized artificially with different noise adding techniques. Here we present an image retrieval algorithm which combines the information of the phase of the Fourier transform of the shoe mark images with the power spectral density of the Fourier transform calculated on their Mahalanobis map. Differently from others, the algorithm performance is tested on real shoe marks coming from crime scenes. The proposed method is compared with other works and some preprocessing operators are also introduced and selected to reduce noise and enhance the matching probability.