Animal Face

Research on animal face recognition focuses on developing automated systems for identifying individual animals, primarily for applications in livestock management and wildlife monitoring. Current efforts concentrate on creating large, annotated datasets of animal faces and designing robust deep learning models, such as parallel attention networks and variations of YOLO and RetinaNet, to address challenges like occlusion and variations in pose and lighting. These advancements improve accuracy in animal identification, enabling more efficient livestock censuses, behavioral studies, and conservation efforts.