Automatic Identification of Individual Primates with Deep Learning Techniques

Summary The difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.
Graphical Abstract
Highlights • The Tri-AI system can rapidly detect and identify individuals from videos and images • Tri-AI had an ID identification accuracy of 94% for 41 primates and 4 carnivores • The system could individually recognize 31 animals/s with images taken day or night • Systems like Tri-AI make around-the-clock monitoring and behavior analysis possible
Zoology; Ethology; Artificial Intelligence