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Measuring the Accuracy of Automatic Shoeprint Recognition Methods
Author(s) -
Luostarinen Tapio,
Lehmussola Antti
Publication year - 2014
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12474
Subject(s) - ransac , artificial intelligence , computer science , crime scene , set (abstract data type) , pattern recognition (psychology) , computer vision , image (mathematics) , geography , archaeology , programming language
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.