Classification of 3D shoe prints using the PointNet architecture: proof of concept investigation of binary classification of nike and adidas outsoles

Abstract

Shoe prints are one of the most common types of evidence found at crime scenes, second only to fingerprints. However, studies involving modern approaches such as machine learning and deep learning for the detection and analysis of shoe prints are quite limited in this field. With advancements in technology, positive results have recently emerged for the detection of 2D shoe prints. However, few studies focusing on 3D shoe prints. This study aims to use deep learning methods, specifically the PointNet architecture, for binary classification applications of 3D shoe prints, utilizing two different shoe brands. A 3D dataset created from 160 pairs of shoes was employed for this research. This dataset comprises 797 images from the Adidas brand and 2445 images from the Nike brand. The dataset used in the study includes worn shoe prints. According to the results obtained, the training phase achieved an accuracy of 96%, and the validation phase achieved an accuracy of 93%. These study results are highly positive and indicate promising potential for classifying 3D shoe prints. This study is described as the first classification study conducted using a deep learning method specifically on 3D shoe prints. It provides proof of concept that deep learning research can be conducted on 3D shoeprints. While the developed binary classification of these 3D shoeprints may not fully meet current forensic needs, it will serve as a source of motivation for future research and for the creation of 3D datasets intended for forensic purposes.