  Photo-identification (photo-id) is one of the main non-invasive
capture-recapture methods utilised by marine researchers for monitoring
cetacean (dolphin, whale, and porpoise) populations. This method has
historically been performed manually resulting in high workload and cost due to
the vast number of images collected. Recently automated aids have been
developed to help speed-up photo-id, although they are often disjoint in their
processing and do not utilise all available identifying information. Work
presented in this paper aims to create a fully automatic photo-id aid capable
of providing most likely matches based on all available information without the
need for data pre-processing such as cropping. This is achieved through a
pipeline of computer vision models and post-processing techniques aimed at
detecting cetaceans in unedited field imagery before passing them downstream
for individual level catalogue matching. The system is capable of handling
previously uncatalogued individuals and flagging these for investigation thanks
to catalogue similarity comparison. We evaluate the system against multiple
real-life photo-id catalogues, achieving<EMAIL_ADDRESS>= 0.91, 0.96 for the task
of dorsal fin detection on catalogues from Tanzania and the UK respectively and
83.1, 97.5% top-10 accuracy for the task of individual classification on
catalogues from the UK and USA.
