Mourad Gueham

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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(More)
In this paper, we propose a solution for the problem of rotated partial shoeprints retrieval based on the combined use of local points of interest and SIFT descriptor. Once the generated features are encoded using SIFT descriptor, matching is carried out using RANSAC to estimate a transformation model and establish the number of its inliers which is then(More)
In this paper, a method for automatically recognizing partial shoeprint images for use in forensic science is presented. The technique uses the Phase-Only Correlation (POC) for shoeprints matching. The main advantage of this method is its capability to match low quality shoeprint images accurately and efficiently. In order to achieve superior performance,(More)
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