Bovine facial recognition through muzzles

This review examines the application of neural network architectures for cattle identification and recognition using snout images as the primary biometric trait, motivated by the anatomical uniqueness and high inter-individual variability of this region. Beyond reporting performance metrics, the analyzed literature consistently highlights that the discriminative power of bovine facial recognition systems is strongly associated with specific visual features, including the spatial configuration of nostrils, texture patterns of the muzzle surface, local curvature of the snout region, and contrast variations under different illumination conditions. A wide range of algorithms and tools, such as SIFT, SURF, KNN, and deep learning architectures including YOLO-based detectors and ResNet-50 backbones, have been employed to extract and model these features effectively. The reviewed studies report high recognition performance, with recent YOLOv8-based approaches achieving accuracies close to or equal to 100% under controlled conditions. Furthermore, the use of transfer learning, convolutional feature hierarchies, and ensemble classifiers, such as random forests, enhances robustness to pose variation, noise, and environmental variability. These findings demonstrate that the success of bovine facial and muzzle recognition is primarily driven by the quality and stability of learned visual features rather than by dataset-specific configurations. As a result, such systems show strong potential for real-time livestock monitoring, health assessment, disease tracking, and scalable precision livestock management, with applicability extending to other animal species.