Measuring the accuracy of automatic shoeprint recognition methods.
Shoeprints are an important source of information for criminal investigation. Therefore, an increasing number of automatic shoeprint recognition methods have been proposed for detecting the corresponding shoe models. However, comprehensive comparisons among the methods have not previously been made. In this study, an extensive set of methods proposed in the literature was implemented, and their performance was studied in varying conditions. Three datasets of different quality shoeprints were used, and the methods were evaluated also with partial and rotated prints. The results show clear differences between the algorithms: while the best performing method, based on local image descriptors and RANSAC, provides rather good results with most of the experiments, some methods are almost completely unrobust against any unidealities in the images. Finally, the results demonstrate that there is still a need for extensive research to improve the accuracy of automatic recognition of crime scene prints.
National Bureau of Investigation, Forensic Laboratory, Jokiniemenkuja 4, 01370, Vantaa, Finland.
Tapio
T
Luostarinen
Antti
A
Lehmussola
automated pattern recognition; automatic shoeprint classification; computer vision; forensic science; performance evaluation; shoeprint evidence