Automatic cattle muzzle print classification system using multiclass support vector machine

Cattle muzzle classification can be considered as a biometric identifier to maintain the livestock and guarantee the safety of cattle products. This paper presents a muzzle-based classification system using multiclass support vector machines (MSVMs). The proposed MSVMs system consists of three phases; namely preprocessing, feature extraction and classifications. Preprocessing techniques, histogram equalisation and mathematical morphology filtering have been used to increase image contrast and removing noise respectively. The proposed system uses box-counting algorithm for detecting feature of each muzzle image. For a strong classification system and achieving more accurate classification result, MSVMs has been used. The experimental evaluation prove the advancement of the presented system as it achieve 96% classification accuracy in case of increase number of classified group to ten groups compared to 90% classification accuracy achieved by traditional classification system.