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

Tharwat, A; Gaber, T; Hassanien, AE

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