CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching

arXiv:2404.16972v1 Announce Type: new
Abstract: Shoeprints are a common type of evidence found at crime scenes and are used regularly in forensic investigations. However, existing methods cannot effectively employ deep learning techniques to match noisy and occluded crime-scene shoeprints to a shoe database due to a lack of training data. Moreover, all existing methods match crime-scene shoeprints to clean reference prints, yet our analysis shows matching to more informative tread depth maps yields better retrieval results. The matching task is …

abstract arxiv crime cs.cv data database deep learning deep learning techniques evidence found however investigations maps match training training data type