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

Citation:
Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier", Computers and Electronics in Agriculture, vol. 122 , issue March 2016 , pp. 55–66, 2016.

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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 algorithIn 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.

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