| Summary: | The Saimaa ringed seal is considered to be endangered and is facing a very high risk of extinction. he conservation efforts largely depend on the ability to track and monitor each individual seal. Photo-identification using camera traps has been successfully used for wildlife monitoring. Each seal has a unique fur pattern that a human expert can match to a specific seal labeled earlier. This thesis focuses on automatic identification of Saimaa ringed seals based on fur pattern extraction. This consists of segmentation of an image with the goal of extracting the seal, extraction of fur pattern from the segmented seal image and searching for the same seal in the seal database. Two methods of Saimaa ringed seal identification based on transfer learning are proposed in this work. The first method involves re-training of the existing convolutional neural network (CNN). The second method involves using the existing CNN trained for image classification as a means to extract features from seal images which are then used to train a Support Vector Machine (SVM) classifier. Both methods are implemented, tested and compared. Both approaches show good results with total accuracy of 91.2% for CNN and 90.5% for SVM.
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