Sexing Caucasian 2D footprints using convolutional neural networks

TL;DR

This study demonstrates that convolutional neural networks can accurately determine the sex of Caucasian 2D footprints with about 90% accuracy, outperforming experts, and highlights the importance of image quality for reliable results.

Contribution

The paper introduces a CNN-based method for sexing 2D footprints and compares it to traditional techniques, showing improved accuracy and potential for automated screening.

Findings

CNN achieves ~90% accuracy on footprint sexing

Image quality significantly affects classification success

Method outperforms expert assessments

Abstract

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N=196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N=2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N=267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.