Abstract
The Environmental Investigation Agency (EIA) possesses a collection ofover 158 distinct tiger skins obtained through encounters with illegal wildlife traders in physical and online markets. Each tiger’s stripes are as unique as fingerprints, and the ultimate goal is to establish a globally accessible method and database that enables law enforcement agencies
and researchers to identify individual tigers based on their stripe patterns. Every skin is associated with one or more traders, and the database also includes confiscated tiger skins and carcasses. Periodically, new skins are added to the database and carefully compared to determine if they are duplicate images or represent the same skin captured from different angles, which can provide valuable insights for law enforcement.
The challenge at hand is to create a user-friendly tool for recognizing individual tigers based on their stripe patterns and utilizing this information to enhance enforcement efforts and combat the illegal trade of tiger skins, carcasses, and live animals.
Citation information
Data Study Group Team. (2023). Environmental Investigation Agency (EIA) Identifying Tiger Stripes with Machine Learning. The Alan Turing Institute. https://doi.org/10.5281/zenodo.10033690
Additional information
Contributers
Kelvin Simatwo is a first year PhD researcher at Loughborough University.
Lara Johnson is a PhD in the School of Informatics at the University of Edinburgh.
Jialin Yu is a research fellow in machine learning at the department of statistical science, University College London.
Zijie Huang is a PhD candidate at the University of Exeter.
Jayesh Choudhari is a Research Scientist at Cube Global Ltd.
Hanzhi Wang is a PhD candidate at the Cardiff University.
Khashayar Ghamati is a PhD candidate at Hertfordshire University.
Nikola Kolev is a PhD candidate at University College London.