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

•Limitations and weakness of traditional cattle identification were highlighted.•Cattle identification approach based on biometric features was proposed.•Weber’s Local Descriptor (WLD) along with many classifiers were examined.•Dataset of muzzle print images (images from 31 cattle animals) were used...

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Bibliographic Details
Published in:Computers and electronics in agriculture 2016-03, Vol.122, p.55-66
Main Authors: Gaber, Tarek, Tharwat, Alaa, Hassanien, Aboul Ella, Snasel, Vaclav
Format: Article
Language:English
Subjects:
Accuracy
AdaBoost classifier
Algorithms
Assessments
Cattle
Cattle identification
Classifiers
Feature extraction
Fuzzy-k-Nearest Neighbor
k-Nearest Neighbor
Machine learning
Muzzle print images
Weber Local Descriptor (WLD)
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Summary:•Limitations and weakness of traditional cattle identification were highlighted.•Cattle identification approach based on biometric features was proposed.•Weber’s Local Descriptor (WLD) along with many classifiers were examined.•Dataset of muzzle print images (images from 31 cattle animals) were used.•The results were validated against False Accept and Reject rate, Sensitivity and Specificity, and accuracy. 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 (Fk-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 Fk-NN algorithms.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2015.12.022