Animal facial recognition (AFR) using artificial intelligence (AI) has emerged as a transformative tool in wildlife conservation, agricultural management, veterinary medicine, and behavioral research. This paper explores the development of AI-driven facial recognition systems for non-human species, highlighting their technical foundations, applications, and ethical implications. By analyzing case studies across diverse animal groups—from primates to livestock—we evaluate the efficacy of machine learning (ML) models like convolutional neural networks (CNNs) and transfer learning in overcoming challenges such as interspecies variability, limited datasets, and dynamic environmental conditions. The paper concludes with recommendations for future research, emphasizing interdisciplinary collaboration and ethical frameworks to maximize the societal and ecological benefits of AFR technologies. We detail the development process, including dataset preparation, model training, and evaluation, and demonstrate its application in identifying individual animals. Our results show high accuracy in recognizing species and individuals, highlighting the feasibility of deploying AFR systems in real-world scenarios. The paper concludes with a discussion of challenges, ethical considerations, and future directions for AFR technologies.
