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Footprints are left, or obtained, in a variety of scenarios from crime scenesto anthropological investigations. Determining the sex of a footprint can beuseful in screening such impressions and attempts have been made to do so usingsingle or multi landmark distances, shape analyses and via the density offriction ridges. Here we explore the relative importance of differentcomponents in sexing two-dimensional foot impressions namely, size, shape andtexture. We use a machine learning approach and compare this to moretraditional methods of discrimination. Two datasets are used, a pilot data setcollected from students at Bournemouth University (N=196) and a larger data setcollected by podiatrists at Sheffield NHS Teaching Hospital (N=2677). Ourconvolutional neural network can sex a footprint with accuracy of around 90% ona test set of N=267 footprint images using all image components, which isbetter than an expert can achieve. However, the quality of the impressionsimpacts on this success rate, but the results are promising and in time it maybe possible to create an automated screening algorithm in which practitionersof whatever sort (medical or forensic) can obtain a first order sexing of atwo-dimensional footprint.

Sexing Caucasian 2D footprints using convolutional neural networks