Abstract-In this paper, we propose a solution of rotated partial shoeprint retrieval, based use of local points of interest and SIFT desc generated features are encoded using S matching is carried out using RANSAC transformation model and establish the num which is then multiplied by the sum of point-t distances below a hard threshold. We demo combination can overcome the issue of ret prints in the presence of rotation and n Conducted experiments have shown that the p achieves very good matching results and out work in the literature both in terms of p complexity. Keywords-shoprint matching; SIFT descrip of ineterest. As a form of physical evidence, a shoem mark made when the sole of a shoe comes a surface, can provide an important lin criminals and the place where the crime o shoeprint lifted from a Scene of Crime checked against a database that includes th on the market to determine its model. It can against other SoC prints and shoeprints ta suspects so that a given shoeprint can be id made by a specific shoe. Several technique have been reported in the literature classification, recognition, indexing and r prints in the presence of rotation and n Chazal et al [4] proposed a system for auto a database of shoeprints based on the outso Fourier domain in response to a referenc shown in [4], the Power Spectral Density (P of the image are calculated using the Fourie used as features. A correlation functio coefficient from a reference database and a used as a similarity metric [4] . To achiev rotation, matching is also carried out with of the query image. It was suggested that should be rotated in the range of [-30 30 rotation step of one degree to achieve rot within that range. That leads to matching copies of the same query image to the refer a brute force approach that attempts to bet score. This drawback is overcome wi is only robust to rotation within the To achieve a high accuracy, the rot filter is robust in [5] is narrower tha method [4] . As such multiple robustness to a wider angle is resolution based techniques have be radon transform is used to estimat angle. A print is divided into nine blocks and convolved with an eig bank. The average variance in each filtered images is used as a fe robustness to partial prints, eight d also processed and included in th create a 9-print class of the same s was used in [7] based on the use o However, in [7] , it is the energy w which is used to build a feature ve energy-based features will perform that was not present in the training in [6] [7] . Following their successful use large databases, model based reco and texture recognition [8] [9] [10] , te image retrieval and classification b features were suggested in [11] . P efficient automated system for i based on using Maximally Stable E features which are transformed usi Although the SIFT descriptor is UK k ated copies of the images e images are rotated to . Still, the designed filter e adopted training range. tation angle to which the an in the case of the PSD filters are required if to be attained. Multi een used in [6]
Nibouche et al. REF use the SIFT descriptor to represent shoeprint images, and the random sample consensus (RANSAC) method is used to estimate the matching performance.
Rotation Invariant Matching of Partial Shoeprints
2009 13th International Machine Vision and Image Processing Conference
2009 13th International Machine Vision and Image Processing Conference
Mathematics Computer Science