Two biometric approaches for cattle identification based on features and classifiers fusion. (2015) - Tharwat, Alaa; Gaber, Tarek; Hassanien, Aboul Ella
According to FAO organisation, by 2050 annual meat production should be risen by over 200 M tonnes to reach 470 M tones as the world population will reach 9.1 billion (34% higher than today). So, there is a 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 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 neighbour, and minimum distance classifier). The experimental results show that, 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 mode achieved accuracy better than the classifier fusion-based model.