Siamese Network Based Pelage Pattern Matching for Ringed Seal Re-identification

In this paper we propose a method to match pelage patterns of the Saimaa ringed seals enabling the re-identification of individuals. First, the pelage pattern is extracted from the seal's fur using a method based on the Sato tubeness filter. After this, the similarities of the pelage pattern patches are computed using a siamese network trained with a triplet loss function and a large dataset of manually selected patches. The similarities are then used to find the best matching patches from the images in the database of known individuals. Furthermore, we employ the proposed pattern matching method to build a full framework for the ringed seal re-identification, consisting of CNN-based animal segmentation, patch correspondence detection, and ranking the images in the database of known seal individuals based on the similarity to the query image. Our experiments on challenging datasets of Saimaa ringed seals show that the proposed method achieves promising identification results, providing a useful tool for the Saimaa ringed seal monitoring.