Investigation on the Muzzle of a Pig as a Biometric for Breed Identification

Chakraborty, Shoubhik; Karthik, Kannan; Banik, S.

doi:10.1007/978-981-32-9088-4_7

Cited by 3 publications

(6 citation statements)

References 3 publications

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“…They prevented low-qulity image problems caused by the use of separate scanning equipment to capture nose patterns by applying sparse representation features and a support vector machine (SVM) classifier. 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.…”

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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“…It was surmised in an earlier work [5], that biometric identifiers tend to be concentrated near sensory interfaces. These sensory interfaces could be the eye (ocular type with the biometric in question being the iris), the ear (aural type dealing with vein patterns on the inner surface), the nose or snout (nasal and tactile type especially for pigs, involving the muzzle) and many others.…”

Section: A Breed Identification and Identifiersmentioning

confidence: 98%

“…Thus, the muzzle segment serves as both a tactile as well as a nasal interface for interacting with the environment. Owing to the presence of many sensory features in virtually the same location, it was concluded in our earlier work [5], that the frontal image of the muzzle covering the heart shaped nasal disc which houses two nostrils could This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: A Breed Identification and Identifiersmentioning

confidence: 99%

“…Most biometrics are found concentrated near sensory interfaces e.g. muzzle of pig [5] or facial profile in cows [1] or hoof patterns in horses. Aggregates of these biometric traces at a macro level, may qualify as what are known as "breed identifiers".…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the focus is on breed identifiers associated with pigs. Visual identifiers and descriptors such as those involving still images of faces of pigs or parts of the face, such as the tip of the snout (termed as the muzzle), have been found to be stable yet discriminative across breeds [5]. The aim of this work is to investigate whether the image of the muzzle of a pig qualifies as a breed identifier for four most popular breeds viz.…”

Section: Introductionmentioning

confidence: 99%

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Graph Synthesis for Pig Breed Classification From Muzzle Images

Chakraborty¹,

Karthik

Banik

2021

IEEE Access

Self Cite

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Non-intrusive and automated detection of pig breeds, particularly from visual standpoint, is important from a food quality tracking perspective, both from the point of view of the vendors as well as the buyers. Colour as well as texture based visual descriptors from muzzle images have been identified, which, serve as breed-identifiers to separate four common pig-breeds: Duroc, Ghungroo, Hampshire and Yorkshire. While these handcrafted visual descriptors by themselves are fairly robust and discriminative, it is recognized that by controlling the decision space either by choosing the feature-type based on colour or texture or by combining features and also selecting the order in which particular breeds are siphoned, classification accuracies can be improved. In that light, is proposed a stable, relatively data-independent, breed-specific, hierarchical tree synthesis and feature selection procedure, based on a breed-pair cluster separation table, aided by some secondary statistics. The proposed approach has been compared with the state of the art Phylogenetic distance based Hierarchical Agglomerative Clustering algorithm (AGNES) and also with the standard decision tree classification algorithm. When completely different sets of pigs were used for training and testing (50-50 split), the proposed algorithm reported relatively high mean classification accuracies of 86.45% for Duroc, 93.02% for Ghungroo, 86.91% for Hampshire and 98.54% for Yorkshire.

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Dog nose print matching with dual global descriptor based on Contrastive Learning

Li¹,

Wang²,

Wu³

et al. 2022

Preprint

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Recent studies in biometric-based identification tasks have shown that deep learning methods can achieve better performance. These methods generally extract the global features as descriptor to represent the original image. Nonetheless, it does not perform well for biometric identification under fine-grained tasks. The main reason is that the single image descriptor contains insufficient information to represent image. In this paper, we present a dual global descriptor model, which combines multiple global descriptors to exploit multi level image features. Moreover, we utilize a contrastive loss to enlarge the distance between image representations of confusing classes. The proposed framework achieves the top2 on the

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Investigation on the Muzzle of a Pig as a Biometric for Breed Identification

Cited by 3 publications

References 3 publications

Dog Nose-Print Identification Using Deep Neural Networks

Dog Nose-Print Identification Using Deep Neural Networks

Graph Synthesis for Pig Breed Classification From Muzzle Images

Dog nose print matching with dual global descriptor based on Contrastive Learning

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