Biometric animal databases from field photographs: identification of individual zebra in the wild

We describe an algorithmic and experimental approach to a fundamental problem in field ecology: computer-assisted individual animal identification. We use a database of noisy photographs taken in the wild to build a biometric database of individual animals differentiated by their coat markings. A new image of an unknown animal can then be queried by its coat markings against the database to determine if the animal has been observed and identified before. Our algorithm, called StripeCodes, efficiently extracts simple image features and uses a dynamic programming algorithm to compare images. We test its accuracy against two different classes of methods: Eigenface, which is based on algebraic techniques, and matching multi-scale histograms of differential image features, an approach from signal processing. StripeCodes performs better than all competing methods for our dataset, and scales well with database size.