A Deep Learning-based Framework for Sheep Identification System based on Facial Bio-Metrics Analysis

Abstract

Through the use of livestock, information sharing is becoming increasingly popular around the world. This study aims to see biometric face analysis be used on sheep recognition to improve sheep monitoring in the centralized database. Anchor-free region convolutional neural networks were used to detect sheep identities (AF-RCNN). Face recognition’s effectiveness as a biometric-based identification for sheep was studied utilizing reviews of face images using the deep earing approach. The method is standalone on a set of standardized facial photos from 50 sheep, using an augmentation strategy to expand the number of sheep images. The proposed method outperforms earlier methods for sheep recognition with high accuracy.