Non-Invasive Cattle Identification Using Siamese Neural Networks with Multi-Head Attention

Animal monitoring, disease prevention, and live-stock management all depend heavily on the identification of the cattles. Traditional methods such as RFID (Radio Frequency Identification) tags and physical branding pose limitations in terms of cost, tampering risk, and animal discomfort. This study proposes a deep learning-based biometric identification system using a Siamese Neural Network augmented with Multi-Head Attention, enabling non-invasive recognition based on unique muzzle textures. The proposed model effectively captures fine-grained spatial variations unique to each animal’s muzzle, leading to high identification accuracy. Both training and evaluation are conducted using the well known standard dataset ThaiBuff-A.The efficacy of the suggested model is evaluated by a variety of evaluation criteria, including the Area Under Curve (AUC), accuracy, recall, and F1-score precision. The experimental results reveal a marked improvement over the baseline Siamese net-work, thereby validating the efficacy of attention-based feature extraction mechanisms in advancing the reliability of cattle identification systems.