A deep learning lightweight model for real-time captive macaque facial recognition based on an improved YOLOX model.

Abstract

Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare. However, reliably identifying individual macaques in group environments remains a significant challenge. This study introduces ACE-YOLOX, a lightweight facial recognition model tailored for captive macaques. ACE-YOLOX incorporates Efficient Channel Attention (ECA), Complete Intersection over Union loss (CIoU), and Adaptive Spatial Feature Fusion (ASFF) into the YOLOX framework, enhancing prediction accuracy while reducing computational complexity. These integrated approaches enable effective multiscale feature extraction. Using a dataset comprising 179 400 labeled facial images from 1 196 macaques, ACE-YOLOX surpassed the performance of classical object detection models, demonstrating superior accuracy and real-time processing capabilities. An Android application was also developed to deploy ACE-YOLOX on smartphones, enabling on-device, real-time macaque recognition. Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool, offering an important foundation for future studies in macaque facial expression recognition, cognitive psychology, and social behavior.