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Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099070" target="_blank" >RIV/61989100:27240/16:86099070 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1016/j.compag.2015.12.022" target="_blank" >http://dx.doi.org/10.1016/j.compag.2015.12.022</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.compag.2015.12.022" target="_blank" >10.1016/j.compag.2015.12.022</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

  • Original language description

    In this paper, we proposed a new and robust biometric-based approach to identify head of cattle. This approach used the Weber Local Descriptor (WLD) to extract robust features from cattle muzzle print images (images from 31 head of cattle were used). It also employed the AdaBoost classifier to identify head of cattle from their WLD features. To validate the results obtained by this classifier, other two classifiers (k-Nearest Neighbor (k-NN) and Fuzzy- k-Nearest Neighbor (F. k-NN)) were used. The experimental results showed that the proposed approach achieved a promising accuracy result (approximately 99.5%) which is better than existed proposed solutions. Moreover, to evaluate the results of the proposed approach, four different assessment methods (Area Under Curve (AUC), Sensitivity and Specificity, accuracy rate, and Equal Error Rate (EER)) were used. The results of all these methods showed that the WLD along with AdaBoost algorithm gave very promising results compared to both of the k-NN and F. k-NN algorithms. (C) 2016 Elsevier B.V..

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Computers and Electronics in Agriculture

  • ISSN

    0168-1699

  • e-ISSN

  • Volume of the periodical

    122

  • Issue of the periodical within the volume

    MAR

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    12

  • Pages from-to

    55-66

  • UT code for WoS article

    000371944900006

  • EID of the result in the Scopus database

    2-s2.0-84955305738