A4 Conference proceedings

Identification of Saimaa Ringed Seal Individuals using Transfer Learning

Publication Details
Authors: Nepovinnykh Ekaterina, Eerola Tuomas, Kälviäinen Heikki, Radchenko Gleb
Publisher: Springer Verlag (Germany): Series
Publication year: 2018
Language: English
Related Journal or Series Information: Lecture Notes in Computer Science
Title of parent publication: Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Poitiers, France, September 24–27, 2018, Proceedings
Journal acronym: LNCS
Volume number: 11182
Start page: 211
End page: 222
Number of pages: 12
ISBN: 978-3-030-01448-3
eISBN: 978-3-030-01449-0
ISSN: 0302-9743
eISSN: 1611-3349
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


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.

Last updated on 2019-13-03 at 12:00

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