A grant has been awarded to Drs. Douglas Bolger and Hany Farid of Dartmouth College to develop tools for the individual recognition of animals. The ability to recognize and follow individual animals over space and time is perhaps the most important tool of animal population biology. Recognizing individuals allows researchers to estimate population size and birth and death rates, and quantify social behavior. These parameters form the basis of most pure and applied population biology. Traditionally, this recognition has been accomplished by capturing animals and placing visible and unique marks on them. These methods are known as mark-recapture or mark-resight. The primary limitations on the use of traditional marking techniques are animal welfare, cost and difficulty. One promising non-invasive technique is the use of photographic ?mark? and resight methods. For animals with unique markings, individuals can be photographed (marked) and the images stored in a database. Animals photographed later can then be compared to the image database to determine if that individual had been seen before (a resight) or if it is new to the study. This method has been used manually for the study of relatively small populations such as those of whales. For use in large populations this image matching process needs to be computer-assisted to be feasible.
Our project is a collaboration between biology and computer science to develop and test an open-source application for individual recognition of animals. This application will include the following modules (1) an image database for the storing and accessing of individual images; (2) several choices of pattern extraction techniques; and (3) several choices of pattern matching algorithm. This system will process digital photographs of individual animals, efficiently extract the essential pattern information, store this information in a database, and efficiently search the existing database for matching images. The feature detectors to be implemented include the Harris detector, steerable filters, and scale invariant feature transform (SIFT). Pattern recognition algorithms will include nearest neighbor, principal components analysis, linear discriminant analysis, and K-means. The system will be tested against our existing mark-resight photographic database of 8,250 wildebeest images from the Tarangire ecosystem in northern Tanzania. Furthermore, we will capture images of two other uniquely patterned ungulates from the same ecosystem: zebra and giraffe. We will use these data and analyses to estimate population size, survival and recruitment for wildebeest. For giraffe and zebra we will be able to estimate population size. In addition to these three African ungulate populations we will also test this system against two collaborator image databases of spotted salamanders and whale sharks. System performance will be evaluated on the basis of the misidentification error rates that it produces for these test databases, as well as the general adaptability of the system for use with different species.
The tools will be developed using common open source applications and will be made available to other researchers through Dartmouth College?s website. In recent years there have been tremendous advances in analytical methods for mark-resight data. These new analytical techniques allow for more accurate parameter estimation and give researchers the ability to rigorously test complex hypotheses. However, the application of these methods has been limited by the relatively small number of populations that have sufficient mark-resight data available. The availability of the tools we create should lead to an increase in at least one to two orders of magnitude in the number of populations that can be monitored and parameterized using photographic mark-resight methodology. This should in turn lead to more informed management and conservation of these animal populations.
