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.
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