Abstract
The conservation efforts of the endangered Saimaa ringed seal depend on the ability to reliably estimate the population size and to track individuals. Wildlife photo-identification has been successfully utilized in monitoring for various species. Traditionally, the collected images have been analyzed by biologists. However, due to the rapid increase in the amount of image data, there is a demand for automated methods. Ringed seals have pelage patterns that are unique to each seal enabling the individual identification. In this work, two methods of Saimaa ringed seal identification based on transfer learning are proposed. The first method involves retraining of an existing convolutional neural network (CNN). The second method uses the CNN trained for image classification to extract features which are then used to train a Support Vector Machine (SVM) classifier. Both approaches show over 90% identification accuracy on challenging image data, the SVM based method being slightly better.
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Acknowledgements
The authors would like to thank Meeri Koivuniemi, Miina Auttila, Riikka Levä-nen, Marja Niemi, and Mervi Kunnasranta from Department of Environmental and Biological Sciences at University of Eastern Finland for providing the database for the experiments and expert knowledge for identifying the individuals.
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Nepovinnykh, E., Eerola, T., Kälviäinen, H., Radchenko, G. (2018). Identification of Saimaa Ringed Seal Individuals Using Transfer Learning. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_18
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