Deep Learning for Automatic Detection and Facial Recognition in Japanese
  Macaques: Illuminating Social Networks
Julien Paulet Axel Molina Benjamin Beltzung Takafumi Suzumura Shinya Yamamoto Cédric Sueur
Individual identification plays a pivotal role in ecology and ethology,
notably as a tool for complex social structures understanding. However,
traditional identification methods often involve invasive physical tags and can
prove both disruptive for animals and time-intensive for researchers. In recent
years, the integration of deep learning in research offered new methodological
perspectives through automatization of complex tasks. Harnessing object
detection and recognition technologies is increasingly used by researchers to
achieve identification on video footage. This study represents a preliminary
exploration into the development of a non-invasive tool for face detection and
individual identification of Japanese macaques (Macaca fuscata) through deep
learning. The ultimate goal of this research is, using identifications done on
the dataset, to automatically generate a social network representation of the
studied population. The current main results are promising: (i) the creation of
a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2%
accuracy and (ii) the creation of an individual recognizer for K{\=o}jima
island macaques population (YOLOv8n model), reaching a 83% accuracy. We also
created a K{\=o}jima population social network by traditional methods, based on
co-occurrences on videos. Thus, we provide a benchmark against which the
automatically generated network will be assessed for reliability. These
preliminary results are a testament to the potential of this innovative
approach to provide the scientific community with a tool for tracking
individuals and social network studies in Japanese macaques.