Facial and Ocular-Based Cattle Biometric Recognition Using Convolutional Neural Networks

Biometric recognition of cattle will be one of the most effective ways of improving livestock management, controlling diseases, and breeding. Traditional methods, such as ear tagging, are invasive, prone to errors, and lost or even tampered with. This paper proposes a biometric recognition system for cattle using facial and ocular features as signatures to identify individual cattle using DNN. It utilizes state-of-the-art CNNs to extract and analyze features from cattle faces and eyes to deliver accurate identification. The use of biometric markers like muzzle patterns, the structure of the eye, and symmetry for meaningful characterization provides an alternative approach to traditional methods that are non-invasive and reliable. Deep learning-based model training on a diverse training dataset of cattle images enables the network to learn robust features that generalize well across different breeds and environmental conditions.