finFindR: Computer-assisted Recognition and Identification of Bottlenose Dolphin Photos in R

Abstract

AbstractPhotographic identification is an essential research and management tool for studying population size and dynamics of common bottlenose dolphins (Tursiops truncatus). Photographic identification involves recognizing individuals based on unique dorsal fin markings. Manual identification of dolphins, while successful, is labor-intensive and time-consuming. To shorten processing times, we developed a series of neural networks that finds fins, assesses their unique characteristics, and matches them to an existing catalog.Our software, finFindR, shortens photo-ID processing times by autonomously finding and isolating (i.e., “cropping”) dolphin fins in raw field photographs, tracing the trailing edge of fins in cropped images, and producing a sorted list of likely identities from a catalog of known individuals. The program then presents users with the top 50 most likely matching identities, allowing users to view side-by-side image pairs and make final identity determinations.During testing on two sets of novel images, finFindR placed the correct individual in the first position of its ordered list in 88% (238/272 and 354/400) of test cases. finFindR placed the correct identity among the top 10 ranked images in 94% of test cases, and among the top 50 ranked images in 97% of test cases. Hence, if a match does not exist in the first 50 images of finFindR’s ordered list, researchers can be almost certain (~97%) that a match does not exist in the entire catalog.During a head-to-head blind test of the human-only and finFindR-assisted matching methods, two experienced photo-ID technicians both achieved 97% correct identification of identities when matched against a catalog containing over 2,000 known individuals. However, the manual-only technician examined 124 images on average before making a match, while the technician using finFindR examined only 10 images on average before finding a match.We conclude that finFindR will facilitate equal or improved match accuracy while greatly reducing the number of examined photos. The faster matches, automated detection, and automated cropping afforded by finFindR will greatly reduce typical photo-ID processing times.