Utilizing farm knowledge for indoor precision livestock farming: Time-domain adaptation of cattle face recognition

In real-field cattle farming environments, precise cattle recognition is imperative for effective animal husbandry practices such as monitoring individual behaviors and screening health to ensure animal welfare. Recently, data-driven deep learning models provide efficient and non-intrusive face recognition. However, their application in real-world scenarios presents significant challenges due to data domain drift over time, encompassing geometric variations in face pose, illumination fluctuations, and disruptions in the background environment. To tackle these challenges, this paper introduces a framework for cattle face recognition with innovative techniques based on farm knowledge that guides the model's training and inference process. First, we combine temporal and pose alignment to mitigate the impact of geometric pose variations. Second, we employ illumination augmentation to adapt to varying illumination conditions, bolstering model robustness. Third, we use semantic segmentation to isolate the facial components, enhancing recognition precision and maintaining focus on facial attributes. Empirical experiments validate our approach, demonstrating its effectiveness for real-world deployment, ensuring robust performance across changing environmental conditions. Our model maintains high accuracy, underscoring its reliability in managing the complexity of real-world scenarios. In summary, this paper presents a comprehensive strategy to address domain drift challenges in cattle face recognition within extended real-world settings, equipping the model to meet the demands of genuine cattle farming contexts effectively.