Classifying recaptured identity documents using the biomedical Meijering and Sato algorithms

Recent research into the domain of identity document recapture detection demonstrated the capability of the Meijering filter, a biomedical image processing algorithm, to detect features present in recaptured documents. Manipulation of identity documents using image processing software is a low-cost, high-risk threat to modern financial systems, opening these institutions to fraud through crimes related to identity theft. In this paper we extend the research into the application of biomedical image processing algorithms, including the Meijering filter and the Sato filter. We build support vector machine and decision tree classifiers based on histograms of images generated from these filters and apply some rudimentary feature reduction techniques. The results show that both filters can be applied to this domain, with the Meijering filter slightly outperforming the Sato filter in most tests