Animal species recognition in the wildlife based on muzzle and shape features using joint CNN

Favorskaya, Margarita N.; Pakhirka, Andrey I.

doi:10.1016/j.procs.2019.09.260

Cited by 34 publications

(7 citation statements)

References 10 publications

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“…The performance of this approach was superior to that of the handcrafted featurebased approach that was previously applied using muzzle print images. Favorskaya and Pakhirka [39] presented animal species identification in the wildlife based on muzzle and shape features using a joint CNN. Hu et al [40] proposed a cow-identification system based on the fusion of deep parts features; they use side-view images, including the head, trunk, and leg parts of the cow, to identify individual cows.…”

Section: B Deep Learning 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: B Deep Learning Feature-based Methodsmentioning

confidence: 99%

Dog Nose-Print Identification Using Deep Neural Networks

Bae

Pak²,

Lee

2021

IEEE Access

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“…To the best of our knowledge, despite the diversity of works found in the related literature, though is possible to find texts that mention the wild boar—for example, References 23,24—none deals with the species in a specific context, as the present article. More specifically, as far as we are concerned, this is the first study that deals with the recognition of the invasive wild boar and both the similar but native species of Brazilian fauna, namely, the collared peccary and white‐lipped peccary.…”

Section: Related Workmentioning

confidence: 96%

Wild boar recognition using convolutional neural networks

Silva

Pádua²,

Ogusuku³

et al. 2020

Concurrency and Computation

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Summary Wild boar (Sus scrofa) is a destructive species of swine. They spread diseases, represent a threat to native species, and destroy natural habitats by destabilizing river banks, thus reducing water flow. The monitoring of populations of wild boars is central to the execution and evaluation of methods to control them. To address this issue, in this article, we retrain and apply four convolutional neural networks (CNNs; AlexNet, VGG‐16, Inception‐v3, and ResNet‐50) to classify different species of “bush pigs” in real‐world footage: two native species of the Brazilian fauna, collared peccary (Pecari tajacu) and white‐lipped peccary (Tayassu pecari), and one invasive species, wild boar (S. scrofa). Results show that CNN can be used to classify animals with very similar behavior and appearance and that ResNet‐50 outperforms all compared CNN in terms of accuracy (98.33%) and the lowest probability of false positives (i.e., native species classified as wild boar).

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“…Development programs in Indonesia are more focused on goats than sheep, even though based on its Muslim religious festivities culture, sheep are often used as sacrifices rather than goats [1]. The surveillance system of wild animals as a non-invasive approach in monitoring animals can now classify and recognize animal species using a camera [2]. Sheep are commercially valued based on their meat or carcass weight and their wool [3].…”

Section: Table Of Contentmentioning

confidence: 99%