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

Tharwat, A, Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191 and Hassanien, AE 2015, 'Two biometric approaches for cattle identification based on features and classifiers fusion' , International Journal of Image Mining, 1 (4) , p. 342.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: International Journal of Image Mining
Publisher: Inderscience
ISSN: 2055-6039
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 11 Sep 2019 10:48
Last Modified: 11 Sep 2019 11:00
URI: http://usir.salford.ac.uk/id/eprint/52283

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