Unique animal identification using deep transfer learning for data fusion in siamese networks

The unique automated identification of animals of various species is a pressing challenge ecologically, environmentally and economically. A broader question relates to how one might exploit the somewhat more mature technologies and techniques used within human visual biometrics to automate this same task for other species. One specific technique is the use of region proposal networks and deep transfer learning in siamese networks for individual animal identification. We report that although it is relatively easy to achieve state of the art performance in uniquely identifying individuals for the easy target of zebras, trying to use the same pipeline to obtain useable, top-10> 85%, results for a more challenging species such as nyala is still an open research problem. We argue that uniquely identifying individuals such as nyala who actively try to disguise themselves in their environments require improved few-shot learning techniques and perhaps more data than the current open dataset we have provided to stimulate this area of research.