Arabian horse identification based on whale optimised multi-class support vector machine

Abstract

In this study, a biometric identification approach for Arabian horse identification is proposed based on the optimised Multi-Class Support Vector Machine (MCSVM). The identification approach is performed in three phases: feature extraction, classification, and optimisation. The feature extraction phase uses Histogram of Oriented Gradient (HOG) to extract features vectors from muzzle print images of the Arabian horses and then stored in the database with its labels. The second phase is the classification phase which uses MCSVM for training and testing classification. Finally in the optimised MCSVM phase, three different swarms, Particle Swarm Optimisation (PSO), Grey Wolf Algorithm (GWA) and Whale Optimisation (WO), are used to optimise MCSVM parameters to enhance the identification accuracy of the Arabian horse. The results obtained show that the MCSVM achieves accuracy of 93.2% and increases to 97.4% with WO algorithm which achieves the best accuracy compared to PSO and GWA.