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Shoe-print image retrieval with multi-part weighted CNN
2019
IEEE Access
Identifying shoe-print impressions in the scene of crime (SoC) from database images is a challenging problem in forensic science due to the complicated impressing surface, the partial absence of on-site impressions, and the huge domain gap between the query and the gallery images. The existing approaches pay much attention to feature extraction while ignoring its distinctive characteristics. In this paper, we propose a novel multi-part weighted convolutional neural network (MP-CNN) for
doi:10.1109/access.2019.2914455
fatcat:ua7w2jylfvaz7oj33pxg4odb5a