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Updated: Oct 23, 2025

Spotting Cheetahs: Identifying Individuals by Their Footprints

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

Sexing white 2D footprints using convolutional neural networks.

Marcin Budka¹, Matthew R Bennett², Sally C Reynolds³

¹Department of Computing and Informatics, Bournemouth University, Poole, United Kingdom.

|August 18, 2021

Summary

Researchers developed a machine learning model to determine sex from 2D footprints with 90% accuracy. This automated approach surpasses expert capabilities, offering promising forensic and medical screening applications.

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Area of Science:

Forensic Science
Biometrics
Machine Learning

Background:

Footprint analysis is crucial in forensic and anthropological investigations.
Previous methods for sex determination from footprints include landmark distances, shape analysis, and friction ridge density.
The relative importance of size, shape, and texture in sexing footprints requires further investigation.

Purpose of the Study:

To explore the significance of size, shape, and texture in determining sex from 2D footprints.
To compare the efficacy of a machine learning approach against traditional methods for footprint sex determination.
To assess the potential for an automated screening algorithm for sexing footprints.

Main Methods:

Utilized a convolutional neural network (CNN) for image analysis.
Employed two datasets: a pilot study (N=196) and a larger clinical dataset (N=2677).
Evaluated the CNN's performance on a test set of 267 footprint images.

Main Results:

The CNN achieved approximately 90% accuracy in sexing footprints using combined image components.
The machine learning model outperformed human expert accuracy.
Impression quality was identified as a factor influencing success rates.

Conclusions:

A machine learning approach, specifically a CNN, demonstrates high accuracy in sex determination from 2D footprints.
This automated method shows potential to exceed human expert performance.
Further development could lead to practical automated screening tools for forensic and medical practitioners.