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Two biometric approaches for cattle identification based on features and classifiers fusion

Tharwat, A; Gaber, T; Hassanien, AE

Authors

A Tharwat

T Gaber

AE Hassanien



Abstract

There is an increasing need for controlling safety policies of animals
and efficient management of food production. One way to help achieve this need
is the automatic animal identification/identification and traceability systems. In
this paper, two biometric models are proposed for cattle identification based on
features and classifiers fusion using Gabor feature extraction technique and the
notion of features and classifiers fusion. Gabor features are first extracted from
three different scales of muzzle print images. Two different levels of fusion are
then used, i.e. feature fusion and classifier fusion, to accurately identify animal
individuals using three different classifiers (Support Vector Machine (SVM), k-
Nearest Neighbor, and Minimum Distance Classifier). The experimental results
showthat the proposed two approaches are robust and accurate in comparing them
with the existed works as the proposed approaches achieve 99.5% identification accuracy. In addition, the results prove that the features fusion-based model
achieved accuracy better than the classifier fusion-based model.

Citation

Tharwat, A., Gaber, T., & Hassanien, A. (2015). Two biometric approaches for cattle identification based on features and classifiers fusion. International journal of image mining (Online), 1(4), 342. https://doi.org/10.1504/IJIM.2015.073902

Journal Article Type Article
Publication Date Dec 30, 2015
Deposit Date Sep 11, 2019
Journal International Journal of Image Mining
Print ISSN 2055-6039
Electronic ISSN 2055-6047
Publisher Inderscience
Volume 1
Issue 4
Pages 342
DOI https://doi.org/10.1504/IJIM.2015.073902
Publisher URL https://doi.org/10.1504/IJIM.2015.073902
Related Public URLs https://www.inderscienceonline.com/loi/ijim