  This report investigates an unsupervised, feature-based image matching
pipeline for the novel application of identifying individual k\=ak\=a. Applied
with a similarity network for clustering, this addresses a weakness of current
supervised approaches to identifying individual birds which struggle to handle
the introduction of new individuals to the population. Our approach uses object
localisation to locate k\=ak\=a within images and then extracts local features
that are invariant to rotation and scale. These features are matched between
images with nearest neighbour matching techniques and mismatch removal to
produce a similarity score for image match comparison. The results show that
matches obtained via the image matching pipeline achieve high accuracy of true
matches. We conclude that feature-based image matching could be used with a
similarity network to provide a viable alternative to existing supervised
approaches.
