Automated Individual Identification of Japanese Macaques (Macaca fuscata) via Deep Learning Facial Recognition and Tracking

This study presents a robust computational framework for the automated detection and identification of Japanese macaques within their natural habitats. By leveraging state-of-the-art deep learning architectures, we propose a two-stage pipeline consisting of facial region detection and individual classification. Our results demonstrate high precision in head localization and promising accuracy in inter-individual identification, providing a scalablesolution for longitudinal ethological research.