A real-time, multi-animal model for automatic face detection and identification of freely moving common marmosets based on YOLOv8 algorithms.

Precise and up-to-date information about animal location and identity allows us to better quantify individual behaviors in studies of neural activity, cognition, and animal health. In socially housed laboratory animals, identification is usually defined by observation or invasive markers, making the data collection time-consuming, variable across experimenters, and disruptive to animals. We established an automatic pipeline for real-time identification of common marmosets in captivity using a close-view camera. It uses the supervised deep-learning YOLOv8 model to localize individuals, detect faces, and classify identities. Moreover, we use recognition of uniquely color-coded collar beads to improve detection accuracy among visually similar individuals. Across adult and juvenile marmosets, our system automatically identifies marmosets with >82.9% precision and >91.5% recall, achieving human-level performance. This pipeline is designed to be easy to use and generalizable across non-human primate species, ages, and recording hardware, providing rapid and automatic identity recognition from real-time video.