Automatic individual identification of Saimaa ringed seals

Chehrsimin, Tina; Eerola, Tuomas; Koivuniemi, Meeri; Auttila, Miina; Levänen, Riikka; Niemi, Marja; Kunnasranta, Mervi; Kälviäinen, Heikki

doi:10.1049/iet-cvi.2017.0082

Cited by 29 publications

(31 citation statements)

References 27 publications

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“…Chakraborty et al [26] used cropped muzzle images of pigs for breed identification; their system involved feature spaces of each of the four pig breeds via gradient significance map (GSM) and maximal likelihood (ML) estimation. Chehrsimin et al [34] considered individual identification via unique pelage patterns of the Saimaa ringed seal. Segmentation and postprocessing were performed to identify target parts.…”

Section: A Handcrafted Feature-based Methodsmentioning

confidence: 99%

Dog Nose-Print Identification Using Deep Neural Networks

Bae

Pak²,

Lee

2021

IEEE Access

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Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of singleperson households. This trend has resulted in a growing interest in problems requiring solutions, such as missing pets and false insurance claims. Traditional non-biometric-based methods cannot address these problems. This paper proposes a novel deep-learning model that can extract discriminative features through dog nose-print patterns for individual identification. We present a robust baseline for how individual dogs can be identified. The proposed dog nose network (DNNet) is a convolutional neural network (CNN)-based Siamese network structure comprising feature extraction and self-attention modules. Moreover, there is no need for a separate scanning device because it uses popular mobile devices to acquire the dataset. Besides high recognition performance, the proposed method also ensures simplicity and efficiency. The proposed method achieves better recognition performance than state-of-the-art methods for the collected dog noseprint dataset. It achieves recognition performance superior to state-of-the-art methods for the collected dog nose-print dataset. Using multiple datasets through cross-validation, we acquired an average identification accuracy of 98.972% with the Rank-1 approach. Additional performance benefits were demonstrated through the receiver operating characteristic (ROC) curve, t-distributed stochastic neighbor embedding (t-SNE), and confusion matrix.

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Section: A Handcrafted Feature-based Methodsmentioning

confidence: 99%

Dog Nose-Print Identification Using Deep Neural Networks

Bae

Pak²,

Lee

2021

IEEE Access

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“…In [27,5,21], the re-identification of the Saimaa ringed seals was considered. In [27], a superpixel based segmentation method and a simple texture feature based ringed seal identification method were presented.…”

Section: Related Work 21 Animal Re-identificationmentioning

confidence: 99%

“…In [27], a superpixel based segmentation method and a simple texture feature based ringed seal identification method were presented. In [5], additional preprocessing steps were proposed and two existing species independent individual identification methods were evaluated. However, the identification performance of neither of the methods is good enough for most practical applications.…”

Section: Related Work 21 Animal Re-identificationmentioning

confidence: 99%

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

Nepovinnykh

Eerola

Kälviäinen

2020

2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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In this paper we propose a method to match pelage patterns of the Saimaa ringed seals enabling the reidentification 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.

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“…As summarized in Table 1, automatic individual identification methods have been studied for a number of species, including African penguins [3], northeast tigers [4], cattle [5], lemurs [6], dairy cows [7], great white sharks [8], pandas [9], primates [10], pigs [11], and ringed seals [12]. Different species usually have largely different appearance; however, different individual animals of the same species may differ quite slightly in their appearance, and can be distinguished only by fine-grained detail.…”

Section: Related Workmentioning

confidence: 99%

“…Identification using microchips is generally more accurate, but as an invasive approach, it is hurtful to red pandas and unfriendly to operate. With the rapid development of computer vision technology in the past decade, some researchers attempt to automatically identify individuals of specific species based on images of the animals [3,4,5,6,7,8,9,10,11,12]. Following the pipeline of typical pattern recognition systems, they extract discriminative features from certain body parts of the animals, compute the similarity scores between images of the animals based on the extracted features, and finally determine the identities of the individual animals in the images according to their similarity scores with the reference images.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Individual Red Pandas from Their Faces

Zhao

Liu

et al. 2019

Lecture Notes in Computer Science

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Individual identification is essential to animal behavior and ecology research and is of significant importance for protecting endangered species. Red pandas, among the world's rarest animals, are currently identified mainly by visual inspection and microelectronic chips, which are costly and inefficient. Motivated by recent advancement in computer-vision-based animal identification, in this paper, we propose an automatic framework for identifying individual red pandas based on their face images. We implement the framework by exploring well-established deep learning models with necessary adaptation for effectively dealing with red panda images. Based on a database of red panda images constructed by ourselves, we evaluate the effectiveness of the proposed automatic individual red panda identification method. The evaluation results show the promising potential of automatically recognizing individual red pandas from their faces. We are going to release our database and model in the public domain to promote the research on automatic animal identification and particularly on the technique for protecting red pandas.

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Automatic individual identification of Saimaa ringed seals

Cited by 29 publications

References 27 publications

Dog Nose-Print Identification Using Deep Neural Networks

Dog Nose-Print Identification Using Deep Neural Networks

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

Distinguishing Individual Red Pandas from Their Faces

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