¹Trakia University, Faculty of Veterinary Medicine, Food Quality and Safety and Veterinary Legislation Department, 6000, Stara Zagora, BULGARIA DOI : 10.9775/kvfd.2025.35661 Precision Livestock Farming (PLF) operates through the implementation of modern technological approaches with regard to monitoring and managing animal health, welfare and productivity of individual animals in real-time. A crucial aspect of PLF is the individual identification of each animal, which contributes to development of personalized decisions, leading to improved health outcomes, optimized feed usage, and greater overall farm efficiency. Currently employed technologies for animal identification include means as ear tags, RFID tags and boluses, neck collars and other devices for identification. Recently, a new promising method for individual identification has emerged, implementing software technologies for animal face recognition. The present paper focuses on the comparison of the currently used methods for identification and animal face recognition on several criteria – accuracy, invasiveness, automation potential, effects on animal welfare and functional challenges. Among the methods analysed, face recognition appeared accurate for over 90%, with high automation potential, non-invasive and excellent outcomes for animal welfare. Although, there are some limitations for the large-scale implementation of this method as hardware costs, light-induced variations and needs for dataset preparation, livestock face identification has the potential to improve the precision and effectiveness of animal husbandry and management. With sustained investment in smart infrastructure for farms and on-field trials, animal face identification could be practically implemented for more efficient and intelligent livestock farming. Keywords : Animal face recognition, Animal identification, Animal welfare, Precision livestock farming