Within the framework of behavioral ecology and conservation, individual recognition plays a critical role in the research on wild social animals at the individual level. Traditional identification methods often rely on long-term field experience or invasive physical tagging. Recent advances in deep learning enable non-invasive individual recognition under natural conditions; however, the effectiveness of facial detection and identification depends on species-specific facial characteristics, environmental conditions, and dataset scale. In this study, we used 3385 images from 18 identified wild Tibetan macaques (Macaca thibetana) to develop an individual recognition system, TMacaque-FaceNet, integrating You Only Look Once (YOLO) for face detection and a Vision Transformer (ViT) for individual classification. The results showed that the Tibetan macaque face detector achieved a mAP@0.5 of 0.971, with a precision of 0.974 and a recall of 0.931. The individual recognizer for the wild Tibetan macaque social group achieved a top-1 accuracy of 96.33% on the test set. On an event-wise (temporal holdout) validation set comprising 90 images (5 images per individual), the recognizer achieved a top-1 accuracy of 95.56%. Gradient-weighted attention rollout analyses further revealed that the model focused on biologically meaningful facial regions, supporting the interpretability of the recognition process. Our results provide a new automated facial recognition method to non-invasively monitor Tibetan macaque individuals in natural environments. It provides a practical tool to facilitate automated behavioral observation, social network analysis, and long-term population monitoring of wild non-human primates.
Gao et al. (Fri,) studied this question.