A robust cattle identification scheme using muzzle print images

Cattle identification receives a great research attention as an important way to maintain the livestock. The identification accuracy and the processing time are two key challenges of any cattle identification methodology. This paper presents a robust and fast cattle identification scheme from muzzle print images using local invariant features. The presented scheme compensates some weakness of ear tag and electrical-based traditional identification techniques in terms of accuracy and processing time. The proposed scheme uses Scale Invariant Feature Transform (SIFT) for detecting the interesting points for image matching. For a robust identification scheme, a Random Sample Consensus (RANSAC) algorithm has been coupled with the SIFT output to remove the outlier points and achieve more robustness. The experimental evaluations prove the superiority of the presented scheme as it achieves 93.3% identification accuracy in reasonable processing time compared to 90% identification accuracy achieved by some traditional identification approaches.